diff --git a/ALD-App/requirements.txt b/ALD-App/requirements.txt
index 4665e9b..cd45409 100644
--- a/ALD-App/requirements.txt
+++ b/ALD-App/requirements.txt
@@ -11,13 +11,13 @@ Flask==1.1.2
 Flask-Compress==1.7.0
 future==0.18.2
 itsdangerous==1.1.0
-Jinja2==2.11.2
+Jinja2==2.11.3
 kiwisolver==1.2.0
 MarkupSafe==1.1.1
 matplotlib==3.3.2
 numpy==1.19.2
 pandas==1.0.3
-Pillow==8.0.0
+Pillow==8.3.2
 plotly==4.11.0
 pyparsing==2.4.7
 python-dateutil==2.8.1
diff --git a/ALD-ML/ALD_ML.ipynb b/ALD-ML/ALD_ML.ipynb
index 21caef2..e7e13c9 100644
--- a/ALD-ML/ALD_ML.ipynb
+++ b/ALD-ML/ALD_ML.ipynb
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {
     "Collapsed": "false"
    },
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {
     "Collapsed": "false"
    },
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {
     "Collapsed": "false"
    },
@@ -118,9 +118,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f7c3e7395194aab89ca632b7a24f59f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(Dropdown(options=('data_plasma_raw.csv', 'Experiment annotation file.csv', 'ID_matching_key_ALD…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "pd.set_option('max_columns', 9)\n",
     "\n",
@@ -179,11 +194,198 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>File name</th>\n",
+       "      <th>Sample ID</th>\n",
+       "      <th>Groups</th>\n",
+       "      <th>Species</th>\n",
+       "      <th>Group2</th>\n",
+       "      <th>Sample type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[1] 20190514_QE10_Evosep1_P0000005_LiNi_SA_Pla...</td>\n",
+       "      <td>Plate1_A1</td>\n",
+       "      <td>QC</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>QC</td>\n",
+       "      <td>Plasma</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[2] 20190514_QE10_Evosep1_P0000005_LiNi_SA_Pla...</td>\n",
+       "      <td>Plate1_A2</td>\n",
+       "      <td>HP</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>HP</td>\n",
+       "      <td>Plasma</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[3] 20190514_QE10_Evosep1_P0000005_LiNi_SA_Pla...</td>\n",
+       "      <td>Plate1_A3</td>\n",
+       "      <td>HP</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>HP</td>\n",
+       "      <td>Plasma</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[4] 20190514_QE10_Evosep1_P0000005_LiNi_SA_Pla...</td>\n",
+       "      <td>Plate1_A4</td>\n",
+       "      <td>ALD</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>ALD</td>\n",
+       "      <td>Plasma</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[5] 20190514_QE10_Evosep1_P0000005_LiNi_SA_Pla...</td>\n",
+       "      <td>Plate1_A5</td>\n",
+       "      <td>ALD</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>ALD</td>\n",
+       "      <td>Plasma</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                           File name  Sample ID Groups  \\\n",
+       "0  [1] 20190514_QE10_Evosep1_P0000005_LiNi_SA_Pla...  Plate1_A1     QC   \n",
+       "1  [2] 20190514_QE10_Evosep1_P0000005_LiNi_SA_Pla...  Plate1_A2     HP   \n",
+       "2  [3] 20190514_QE10_Evosep1_P0000005_LiNi_SA_Pla...  Plate1_A3     HP   \n",
+       "3  [4] 20190514_QE10_Evosep1_P0000005_LiNi_SA_Pla...  Plate1_A4    ALD   \n",
+       "4  [5] 20190514_QE10_Evosep1_P0000005_LiNi_SA_Pla...  Plate1_A5    ALD   \n",
+       "\n",
+       "  Species Group2 Sample type  \n",
+       "0   Human     QC      Plasma  \n",
+       "1   Human     HP      Plasma  \n",
+       "2   Human     HP      Plasma  \n",
+       "3   Human    ALD      Plasma  \n",
+       "4   Human    ALD      Plasma  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>File name</th>\n",
+       "      <th>Sample ID</th>\n",
+       "      <th>Groups</th>\n",
+       "      <th>Species</th>\n",
+       "      <th>Group2</th>\n",
+       "      <th>Sample type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>603</td>\n",
+       "      <td>603</td>\n",
+       "      <td>603</td>\n",
+       "      <td>603</td>\n",
+       "      <td>603</td>\n",
+       "      <td>603</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>603</td>\n",
+       "      <td>603</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>[391] 20190531_QE10_Evosep1_P0000005_LiNi_SA_P...</td>\n",
+       "      <td>Plate1_E11</td>\n",
+       "      <td>ALD</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>ALD</td>\n",
+       "      <td>Plasma</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>355</td>\n",
+       "      <td>603</td>\n",
+       "      <td>459</td>\n",
+       "      <td>603</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                File name   Sample ID Groups  \\\n",
+       "count                                                 603         603    603   \n",
+       "unique                                                603         603      4   \n",
+       "top     [391] 20190531_QE10_Evosep1_P0000005_LiNi_SA_P...  Plate1_E11    ALD   \n",
+       "freq                                                    1           1    355   \n",
+       "\n",
+       "       Species Group2 Sample type  \n",
+       "count      603    603         603  \n",
+       "unique       1      3           1  \n",
+       "top      Human    ALD      Plasma  \n",
+       "freq       603    459         603  "
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "annotation_file = pd.read_csv(os.path.join(FOLDER_DATA_RAW, 'Experiment annotation file.csv'), index_col = [0])\n",
     "annotation_file_plasma = annotation_file[annotation_file['Sample type'] == 'Plasma']\n",
@@ -201,11 +403,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       Plate1_A1\n",
+       "1       Plate1_A2\n",
+       "2       Plate1_A3\n",
+       "3       Plate1_A4\n",
+       "4       Plate1_A5\n",
+       "          ...    \n",
+       "598    Plate7_C10\n",
+       "599    Plate7_C11\n",
+       "600    Plate7_C12\n",
+       "601     Plate7_D1\n",
+       "602     Plate7_D2\n",
+       "Name: Sample ID, Length: 603, dtype: object"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "annotation_file_plasma[\"Sample ID\"]"
    ]
@@ -219,11 +443,174 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Protein ID</th>\n",
+       "      <th>Gene names</th>\n",
+       "      <th>[1] 20190514_QE10_Evosep1_P0000005_LiNi_SA_Plate1_A1.htrms.PG.NrOfStrippedSequencesUsedForQuantification</th>\n",
+       "      <th>[2] 20190514_QE10_Evosep1_P0000005_LiNi_SA_Plate1_A2.htrms.PG.NrOfStrippedSequencesUsedForQuantification</th>\n",
+       "      <th>...</th>\n",
+       "      <th>[600] 20190618_QE10_Evosep1_P0000005_LiNi_SA_Plate7_C11.htrms.PG.Quantity</th>\n",
+       "      <th>[601] 20190618_QE10_Evosep1_P0000005_LiNi_SA_Plate7_C12.htrms.PG.Quantity</th>\n",
+       "      <th>[602] 20190618_QE10_Evosep1_P0000005_LiNi_SA_Plate7_D1.htrms.PG.Quantity</th>\n",
+       "      <th>[603] 20190618_QE10_Evosep1_P0000005_LiNi_SA_Plate7_D2.htrms.PG.Quantity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A0A024R6I7</td>\n",
+       "      <td>SERPINA1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.113509e+03</td>\n",
+       "      <td>2.998578e+03</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.988878e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A0A075B6I0</td>\n",
+       "      <td>IGLV8-61</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.722763e+05</td>\n",
+       "      <td>3.496285e+05</td>\n",
+       "      <td>3.066827e+05</td>\n",
+       "      <td>3.786927e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A0A075B6J9</td>\n",
+       "      <td>IGLV2-18</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.596762e+04</td>\n",
+       "      <td>1.363557e+05</td>\n",
+       "      <td>1.330743e+05</td>\n",
+       "      <td>6.273911e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>A0A075B6R9;A0A0C4DH68</td>\n",
+       "      <td>IGKV2D-24;IGKV2-24</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.784640e+04</td>\n",
+       "      <td>3.716600e+05</td>\n",
+       "      <td>1.769536e+05</td>\n",
+       "      <td>1.536745e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>A0A075B6S2;A2NJV5</td>\n",
+       "      <td>IGKV2D-29;IGKV2-29</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.905379e+06</td>\n",
+       "      <td>4.987740e+06</td>\n",
+       "      <td>5.406188e+06</td>\n",
+       "      <td>6.198768e+06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 1208 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Protein ID          Gene names  \\\n",
+       "0             A0A024R6I7            SERPINA1   \n",
+       "1             A0A075B6I0            IGLV8-61   \n",
+       "2             A0A075B6J9            IGLV2-18   \n",
+       "3  A0A075B6R9;A0A0C4DH68  IGKV2D-24;IGKV2-24   \n",
+       "4      A0A075B6S2;A2NJV5  IGKV2D-29;IGKV2-29   \n",
+       "\n",
+       "   [1] 20190514_QE10_Evosep1_P0000005_LiNi_SA_Plate1_A1.htrms.PG.NrOfStrippedSequencesUsedForQuantification  \\\n",
+       "0                                                1.0                                                          \n",
+       "1                                                1.0                                                          \n",
+       "2                                                1.0                                                          \n",
+       "3                                                1.0                                                          \n",
+       "4                                                1.0                                                          \n",
+       "\n",
+       "   [2] 20190514_QE10_Evosep1_P0000005_LiNi_SA_Plate1_A2.htrms.PG.NrOfStrippedSequencesUsedForQuantification  \\\n",
+       "0                                                1.0                                                          \n",
+       "1                                                1.0                                                          \n",
+       "2                                                1.0                                                          \n",
+       "3                                                1.0                                                          \n",
+       "4                                                1.0                                                          \n",
+       "\n",
+       "   ...  \\\n",
+       "0  ...   \n",
+       "1  ...   \n",
+       "2  ...   \n",
+       "3  ...   \n",
+       "4  ...   \n",
+       "\n",
+       "   [600] 20190618_QE10_Evosep1_P0000005_LiNi_SA_Plate7_C11.htrms.PG.Quantity  \\\n",
+       "0                                       4.113509e+03                           \n",
+       "1                                       4.722763e+05                           \n",
+       "2                                       3.596762e+04                           \n",
+       "3                                       9.784640e+04                           \n",
+       "4                                       3.905379e+06                           \n",
+       "\n",
+       "   [601] 20190618_QE10_Evosep1_P0000005_LiNi_SA_Plate7_C12.htrms.PG.Quantity  \\\n",
+       "0                                       2.998578e+03                           \n",
+       "1                                       3.496285e+05                           \n",
+       "2                                       1.363557e+05                           \n",
+       "3                                       3.716600e+05                           \n",
+       "4                                       4.987740e+06                           \n",
+       "\n",
+       "   [602] 20190618_QE10_Evosep1_P0000005_LiNi_SA_Plate7_D1.htrms.PG.Quantity  \\\n",
+       "0                                                NaN                          \n",
+       "1                                       3.066827e+05                          \n",
+       "2                                       1.330743e+05                          \n",
+       "3                                       1.769536e+05                          \n",
+       "4                                       5.406188e+06                          \n",
+       "\n",
+       "   [603] 20190618_QE10_Evosep1_P0000005_LiNi_SA_Plate7_D2.htrms.PG.Quantity  \n",
+       "0                                       1.988878e+06                         \n",
+       "1                                       3.786927e+05                         \n",
+       "2                                       6.273911e+05                         \n",
+       "3                                       1.536745e+05                         \n",
+       "4                                       6.198768e+06                         \n",
+       "\n",
+       "[5 rows x 1208 columns]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "report_plasma = pd.read_csv(os.path.join(FOLDER_DATA_RAW, '20190620_210717_20190620_P0000005_Lili2Klibrary_Report.csv'), na_values='Filtered')\n",
     "report_plasma.rename({'PG.Genes': 'Gene names', 'PG.ProteinAccessions': 'Protein ID'}, inplace= True, axis=1)\n",
@@ -239,11 +626,80 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Protein ID</th>\n",
+       "      <th>Gene names</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[A0A024R6I7]</td>\n",
+       "      <td>[SERPINA1]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[A0A075B6I0]</td>\n",
+       "      <td>[IGLV8-61]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[A0A075B6J9]</td>\n",
+       "      <td>[IGLV2-18]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[A0A075B6R9, A0A0C4DH68]</td>\n",
+       "      <td>[IGKV2D-24, IGKV2-24]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[A0A075B6S2, A2NJV5]</td>\n",
+       "      <td>[IGKV2D-29, IGKV2-29]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Protein ID             Gene names\n",
+       "0              [A0A024R6I7]             [SERPINA1]\n",
+       "1              [A0A075B6I0]             [IGLV8-61]\n",
+       "2              [A0A075B6J9]             [IGLV2-18]\n",
+       "3  [A0A075B6R9, A0A0C4DH68]  [IGKV2D-24, IGKV2-24]\n",
+       "4      [A0A075B6S2, A2NJV5]  [IGKV2D-29, IGKV2-29]"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "columns_ = ['Protein ID', 'Gene names']\n",
     "ids_ = report_plasma[columns_].apply(lambda series_: series_.str.split(';'))\n",
@@ -252,11 +708,119 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Protein ID</th>\n",
+       "      <th>Gene names</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>519</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>520</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>521</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>522</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>523</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>524 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Protein ID  Gene names\n",
+       "0             1           1\n",
+       "1             1           1\n",
+       "2             1           1\n",
+       "3             2           2\n",
+       "4             2           2\n",
+       "..          ...         ...\n",
+       "519           1           1\n",
+       "520           1           1\n",
+       "521           1           1\n",
+       "522           1           1\n",
+       "523           1           1\n",
+       "\n",
+       "[524 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "def length_(x):\n",
     "    try:\n",
@@ -270,11 +834,140 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Protein ID</th>\n",
+       "      <th>Gene names</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>318.0</td>\n",
+       "      <td>499.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>121.0</td>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>44.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Protein ID  Gene names\n",
+       "0          NaN         2.0\n",
+       "1        318.0       499.0\n",
+       "2        121.0        11.0\n",
+       "3         44.0         3.0\n",
+       "4         20.0         2.0\n",
+       "5          7.0         3.0\n",
+       "6          6.0         1.0\n",
+       "7          1.0         NaN\n",
+       "8          1.0         NaN\n",
+       "10         1.0         NaN\n",
+       "11         1.0         NaN\n",
+       "12         1.0         1.0\n",
+       "14         1.0         1.0\n",
+       "16         1.0         NaN\n",
+       "17         1.0         1.0"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from src.pandas import combine_value_counts\n",
     "combine_value_counts(count_groups_proteins)"
@@ -292,16 +985,232 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Protein ID</th>\n",
+       "      <th>Gene names</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[A0A075B6R9, A0A0C4DH68]</td>\n",
+       "      <td>[IGKV2D-24, IGKV2-24]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[A0A075B6S2, A2NJV5]</td>\n",
+       "      <td>[IGKV2D-29, IGKV2-29]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>[A0A087WWU8, P06753-2, Q5HYB6]</td>\n",
+       "      <td>[TPM3, TPM3, DKFZp686J1372]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>[A0A0A0MRZ8, P04433]</td>\n",
+       "      <td>[IGKV3D-11, IGKV3-11]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>[A0A0C4DH43, A0A0J9YVU5]</td>\n",
+       "      <td>[IGHV2-70D, IGHV2-70]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>[A0A0C4DH72, P01599]</td>\n",
+       "      <td>[IGKV1-6, IGKV1-17]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>[A0A0G2JIW1, P0DMV8, P0DMV9]</td>\n",
+       "      <td>[HSPA1B, HSPA1A, HSPA1B]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>[A0A0J9YY99, P01764, P01768, P0DP03]</td>\n",
+       "      <td>[, IGHV3-23, IGHV3-30, IGHV3-30-5]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>[A0A0U1RR32, A0A0U1RRH7, P04908, P0C0S8, P2067...</td>\n",
+       "      <td>[HIST1H3D, HIST1H3D, HIST1H2AB, HIST1H2AG, HIS...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>[B4DV12, F5GXK7, F5GYU3, F5H265, F5H2Z3, F5H38...</td>\n",
+       "      <td>[UBB, UBC, UBC, UBC, UBC, UBC, UBC, UBC, UBB, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>[B4E1Z4]</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>[B5MCK8, P19440, P36268, P36268-2, P36268-3]</td>\n",
+       "      <td>[GGT2, GGT1, GGT2, GGT2, GGT2]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>[E9PQ80, E9PQI5, E9PSI1, Q9BY43, Q9BY43-2]</td>\n",
+       "      <td>[CHMP4A, CHMP4A, , CHMP4A, CHMP4A]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108</th>\n",
+       "      <td>[F8W6I7, P09651, P09651-2, P09651-3, Q32P51]</td>\n",
+       "      <td>[HNRNPA1, HNRNPA1, HNRNPA1, HNRNPA1, HNRNPA1L2]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>[H0YJW9]</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>[H3BMH2, H3BSC1, P62491, P62491-2, Q15907, Q15...</td>\n",
+       "      <td>[RAB11A, RAB11A, RAB11A, RAB11A, RAB11B, RAB11B]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>[H7BY64, Q96NZ9, Q96NZ9-2, Q96NZ9-4]</td>\n",
+       "      <td>[, PRAP1, PRAP1, PRAP1]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>123</th>\n",
+       "      <td>[H7BZ24, P05451, P48304]</td>\n",
+       "      <td>[REG1B, REG1A, REG1B]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>[O60814, P06899, P23527, P33778, P57053, P5887...</td>\n",
+       "      <td>[HIST1H2BK, HIST1H2BJ, HIST1H2BO, HIST1H2BB, H...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>[P01593, P01594]</td>\n",
+       "      <td>[IGKV1D-33, IGKV1-33]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>188</th>\n",
+       "      <td>[P01717, P01718]</td>\n",
+       "      <td>[IGLV3-25, IGLV3-27]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191</th>\n",
+       "      <td>[P01782, P0DP04]</td>\n",
+       "      <td>[IGHV3-9, IGHV3-43D]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>220</th>\n",
+       "      <td>[P02776, P10720]</td>\n",
+       "      <td>[PF4, PF4V1]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>292</th>\n",
+       "      <td>[P0DOY2, P0DOY3]</td>\n",
+       "      <td>[IGLC2, IGLC3]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>404</th>\n",
+       "      <td>[P68104, Q5VTE0]</td>\n",
+       "      <td>[EEF1A1, EEF1A1P5]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            Protein ID  \\\n",
+       "3                             [A0A075B6R9, A0A0C4DH68]   \n",
+       "4                                 [A0A075B6S2, A2NJV5]   \n",
+       "8                       [A0A087WWU8, P06753-2, Q5HYB6]   \n",
+       "21                                [A0A0A0MRZ8, P04433]   \n",
+       "36                            [A0A0C4DH43, A0A0J9YVU5]   \n",
+       "37                                [A0A0C4DH72, P01599]   \n",
+       "40                        [A0A0G2JIW1, P0DMV8, P0DMV9]   \n",
+       "44                [A0A0J9YY99, P01764, P01768, P0DP03]   \n",
+       "48   [A0A0U1RR32, A0A0U1RRH7, P04908, P0C0S8, P2067...   \n",
+       "70   [B4DV12, F5GXK7, F5GYU3, F5H265, F5H2Z3, F5H38...   \n",
+       "71                                            [B4E1Z4]   \n",
+       "73        [B5MCK8, P19440, P36268, P36268-2, P36268-3]   \n",
+       "99          [E9PQ80, E9PQI5, E9PSI1, Q9BY43, Q9BY43-2]   \n",
+       "108       [F8W6I7, P09651, P09651-2, P09651-3, Q32P51]   \n",
+       "119                                           [H0YJW9]   \n",
+       "120  [H3BMH2, H3BSC1, P62491, P62491-2, Q15907, Q15...   \n",
+       "122               [H7BY64, Q96NZ9, Q96NZ9-2, Q96NZ9-4]   \n",
+       "123                           [H7BZ24, P05451, P48304]   \n",
+       "145  [O60814, P06899, P23527, P33778, P57053, P5887...   \n",
+       "183                                   [P01593, P01594]   \n",
+       "188                                   [P01717, P01718]   \n",
+       "191                                   [P01782, P0DP04]   \n",
+       "220                                   [P02776, P10720]   \n",
+       "292                                   [P0DOY2, P0DOY3]   \n",
+       "404                                   [P68104, Q5VTE0]   \n",
+       "\n",
+       "                                            Gene names  \n",
+       "3                                [IGKV2D-24, IGKV2-24]  \n",
+       "4                                [IGKV2D-29, IGKV2-29]  \n",
+       "8                          [TPM3, TPM3, DKFZp686J1372]  \n",
+       "21                               [IGKV3D-11, IGKV3-11]  \n",
+       "36                               [IGHV2-70D, IGHV2-70]  \n",
+       "37                                 [IGKV1-6, IGKV1-17]  \n",
+       "40                            [HSPA1B, HSPA1A, HSPA1B]  \n",
+       "44                  [, IGHV3-23, IGHV3-30, IGHV3-30-5]  \n",
+       "48   [HIST1H3D, HIST1H3D, HIST1H2AB, HIST1H2AG, HIS...  \n",
+       "70   [UBB, UBC, UBC, UBC, UBC, UBC, UBC, UBC, UBB, ...  \n",
+       "71                                                 NaN  \n",
+       "73                      [GGT2, GGT1, GGT2, GGT2, GGT2]  \n",
+       "99                  [CHMP4A, CHMP4A, , CHMP4A, CHMP4A]  \n",
+       "108    [HNRNPA1, HNRNPA1, HNRNPA1, HNRNPA1, HNRNPA1L2]  \n",
+       "119                                                NaN  \n",
+       "120   [RAB11A, RAB11A, RAB11A, RAB11A, RAB11B, RAB11B]  \n",
+       "122                            [, PRAP1, PRAP1, PRAP1]  \n",
+       "123                              [REG1B, REG1A, REG1B]  \n",
+       "145  [HIST1H2BK, HIST1H2BJ, HIST1H2BO, HIST1H2BB, H...  \n",
+       "183                              [IGKV1D-33, IGKV1-33]  \n",
+       "188                               [IGLV3-25, IGLV3-27]  \n",
+       "191                               [IGHV3-9, IGHV3-43D]  \n",
+       "220                                       [PF4, PF4V1]  \n",
+       "292                                     [IGLC2, IGLC3]  \n",
+       "404                                 [EEF1A1, EEF1A1P5]  "
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ids_.loc[count_groups_proteins['Gene names'] != 1]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
    "metadata": {
     "Collapsed": "false"
    },
@@ -313,7 +1222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "metadata": {
     "Collapsed": "false"
    },
@@ -326,18 +1235,251 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>[1] 20190514_QE10_Evosep1_P0000005_LiNi_SA_Plate1_A1.htrms.PG.NrOfStrippedSequencesUsedForQuantification</th>\n",
+       "      <th>[2] 20190514_QE10_Evosep1_P0000005_LiNi_SA_Plate1_A2.htrms.PG.NrOfStrippedSequencesUsedForQuantification</th>\n",
+       "      <th>[3] 20190514_QE10_Evosep1_P0000005_LiNi_SA_Plate1_A3.htrms.PG.NrOfStrippedSequencesUsedForQuantification</th>\n",
+       "      <th>[4] 20190514_QE10_Evosep1_P0000005_LiNi_SA_Plate1_A4.htrms.PG.NrOfStrippedSequencesUsedForQuantification</th>\n",
+       "      <th>...</th>\n",
+       "      <th>[600] 20190618_QE10_Evosep1_P0000005_LiNi_SA_Plate7_C11.htrms.PG.Quantity</th>\n",
+       "      <th>[601] 20190618_QE10_Evosep1_P0000005_LiNi_SA_Plate7_C12.htrms.PG.Quantity</th>\n",
+       "      <th>[602] 20190618_QE10_Evosep1_P0000005_LiNi_SA_Plate7_D1.htrms.PG.Quantity</th>\n",
+       "      <th>[603] 20190618_QE10_Evosep1_P0000005_LiNi_SA_Plate7_D2.htrms.PG.Quantity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>361.000000</td>\n",
+       "      <td>327.000000</td>\n",
+       "      <td>317.000000</td>\n",
+       "      <td>340.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.640000e+02</td>\n",
+       "      <td>3.200000e+02</td>\n",
+       "      <td>3.130000e+02</td>\n",
+       "      <td>3.220000e+02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2.124654</td>\n",
+       "      <td>2.171254</td>\n",
+       "      <td>2.192429</td>\n",
+       "      <td>2.191176</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.939520e+06</td>\n",
+       "      <td>4.541551e+06</td>\n",
+       "      <td>4.727407e+06</td>\n",
+       "      <td>4.267386e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.933022</td>\n",
+       "      <td>0.927444</td>\n",
+       "      <td>0.912747</td>\n",
+       "      <td>0.903245</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.108969e+07</td>\n",
+       "      <td>4.513567e+07</td>\n",
+       "      <td>4.711617e+07</td>\n",
+       "      <td>4.104998e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.908130e+02</td>\n",
+       "      <td>5.826772e+02</td>\n",
+       "      <td>7.995712e+02</td>\n",
+       "      <td>2.039593e+02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.566062e+03</td>\n",
+       "      <td>1.047221e+04</td>\n",
+       "      <td>1.268499e+04</td>\n",
+       "      <td>1.084710e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.154654e+04</td>\n",
+       "      <td>6.404788e+04</td>\n",
+       "      <td>6.787159e+04</td>\n",
+       "      <td>6.053365e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.774295e+05</td>\n",
+       "      <td>6.070775e+05</td>\n",
+       "      <td>7.744870e+05</td>\n",
+       "      <td>6.438288e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.727070e+08</td>\n",
+       "      <td>7.973126e+08</td>\n",
+       "      <td>8.239611e+08</td>\n",
+       "      <td>7.269720e+08</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 1206 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       [1] 20190514_QE10_Evosep1_P0000005_LiNi_SA_Plate1_A1.htrms.PG.NrOfStrippedSequencesUsedForQuantification  \\\n",
+       "count                                         361.000000                                                          \n",
+       "mean                                            2.124654                                                          \n",
+       "std                                             0.933022                                                          \n",
+       "min                                             1.000000                                                          \n",
+       "25%                                             1.000000                                                          \n",
+       "50%                                             3.000000                                                          \n",
+       "75%                                             3.000000                                                          \n",
+       "max                                             3.000000                                                          \n",
+       "\n",
+       "       [2] 20190514_QE10_Evosep1_P0000005_LiNi_SA_Plate1_A2.htrms.PG.NrOfStrippedSequencesUsedForQuantification  \\\n",
+       "count                                         327.000000                                                          \n",
+       "mean                                            2.171254                                                          \n",
+       "std                                             0.927444                                                          \n",
+       "min                                             1.000000                                                          \n",
+       "25%                                             1.000000                                                          \n",
+       "50%                                             3.000000                                                          \n",
+       "75%                                             3.000000                                                          \n",
+       "max                                             3.000000                                                          \n",
+       "\n",
+       "       [3] 20190514_QE10_Evosep1_P0000005_LiNi_SA_Plate1_A3.htrms.PG.NrOfStrippedSequencesUsedForQuantification  \\\n",
+       "count                                         317.000000                                                          \n",
+       "mean                                            2.192429                                                          \n",
+       "std                                             0.912747                                                          \n",
+       "min                                             1.000000                                                          \n",
+       "25%                                             1.000000                                                          \n",
+       "50%                                             3.000000                                                          \n",
+       "75%                                             3.000000                                                          \n",
+       "max                                             3.000000                                                          \n",
+       "\n",
+       "       [4] 20190514_QE10_Evosep1_P0000005_LiNi_SA_Plate1_A4.htrms.PG.NrOfStrippedSequencesUsedForQuantification  \\\n",
+       "count                                         340.000000                                                          \n",
+       "mean                                            2.191176                                                          \n",
+       "std                                             0.903245                                                          \n",
+       "min                                             1.000000                                                          \n",
+       "25%                                             1.000000                                                          \n",
+       "50%                                             3.000000                                                          \n",
+       "75%                                             3.000000                                                          \n",
+       "max                                             3.000000                                                          \n",
+       "\n",
+       "       ...  \\\n",
+       "count  ...   \n",
+       "mean   ...   \n",
+       "std    ...   \n",
+       "min    ...   \n",
+       "25%    ...   \n",
+       "50%    ...   \n",
+       "75%    ...   \n",
+       "max    ...   \n",
+       "\n",
+       "       [600] 20190618_QE10_Evosep1_P0000005_LiNi_SA_Plate7_C11.htrms.PG.Quantity  \\\n",
+       "count                                       3.640000e+02                           \n",
+       "mean                                        3.939520e+06                           \n",
+       "std                                         4.108969e+07                           \n",
+       "min                                         1.908130e+02                           \n",
+       "25%                                         8.566062e+03                           \n",
+       "50%                                         4.154654e+04                           \n",
+       "75%                                         4.774295e+05                           \n",
+       "max                                         7.727070e+08                           \n",
+       "\n",
+       "       [601] 20190618_QE10_Evosep1_P0000005_LiNi_SA_Plate7_C12.htrms.PG.Quantity  \\\n",
+       "count                                       3.200000e+02                           \n",
+       "mean                                        4.541551e+06                           \n",
+       "std                                         4.513567e+07                           \n",
+       "min                                         5.826772e+02                           \n",
+       "25%                                         1.047221e+04                           \n",
+       "50%                                         6.404788e+04                           \n",
+       "75%                                         6.070775e+05                           \n",
+       "max                                         7.973126e+08                           \n",
+       "\n",
+       "       [602] 20190618_QE10_Evosep1_P0000005_LiNi_SA_Plate7_D1.htrms.PG.Quantity  \\\n",
+       "count                                       3.130000e+02                          \n",
+       "mean                                        4.727407e+06                          \n",
+       "std                                         4.711617e+07                          \n",
+       "min                                         7.995712e+02                          \n",
+       "25%                                         1.268499e+04                          \n",
+       "50%                                         6.787159e+04                          \n",
+       "75%                                         7.744870e+05                          \n",
+       "max                                         8.239611e+08                          \n",
+       "\n",
+       "       [603] 20190618_QE10_Evosep1_P0000005_LiNi_SA_Plate7_D2.htrms.PG.Quantity  \n",
+       "count                                       3.220000e+02                         \n",
+       "mean                                        4.267386e+06                         \n",
+       "std                                         4.104998e+07                         \n",
+       "min                                         2.039593e+02                         \n",
+       "25%                                         1.084710e+04                         \n",
+       "50%                                         6.053365e+04                         \n",
+       "75%                                         6.438288e+05                         \n",
+       "max                                         7.269720e+08                         \n",
+       "\n",
+       "[8 rows x 1206 columns]"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "report_plasma.describe()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
    "metadata": {
     "Collapsed": "false"
    },
@@ -357,7 +1499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 56,
    "metadata": {
     "Collapsed": "false"
    },
@@ -379,11 +1521,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 57,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(524, 603)"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "data_plasma_raw = data_plasma_raw.rename(mapper = map_filenames_ids, axis=1)\n",
     "IDmapping_UniprotID_to_Genename = dict(zip(data_plasma_raw['Protein ID'], data_plasma_raw['Gene names']))\n",
@@ -393,11 +1546,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 58,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "304"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "mask = data_plasma_raw.notna().sum(axis=1) > 603 * 0.6\n",
     "mask.sum()"
@@ -414,7 +1578,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 59,
    "metadata": {
     "Collapsed": "false"
    },
@@ -434,9 +1598,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 60,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Plate1_E1     80\n",
+       "Plate7_B6    114\n",
+       "Plate4_E3    117\n",
+       "Plate3_H2    119\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "mask_filtered_out = data_plasma_filtered.notna().sum() < 200\n",
     "data_plasma_filtered.loc[:, list(mask_filtered_out)].describe().loc['count'].astype(int).sort_values()"
@@ -444,9 +1623,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 61,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Plate1_E1     85\n",
+       "Plate4_E3    121\n",
+       "Plate3_H2    127\n",
+       "Plate7_B6    169\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "data_plasma_raw.loc[:, mask_filtered_out].describe().loc['count'].astype(int).sort_values()"
    ]
@@ -469,12 +1663,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
    "metadata": {
     "Collapsed": "false",
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Min No. of Protein-Groups in single sample: 200, i.e. a fraction of 0.6578947368421053\n"
+     ]
+    }
+   ],
    "source": [
     "MIN_N_PROTEIN_GROUPS = 200\n",
     "print(f\"Min No. of Protein-Groups in single sample: {MIN_N_PROTEIN_GROUPS}, i.e. a fraction of {MIN_N_PROTEIN_GROUPS/len(data_plasma_filtered)}\")"
@@ -482,7 +1684,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 63,
    "metadata": {
     "Collapsed": "false"
    },
@@ -494,7 +1696,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 64,
    "metadata": {
     "Collapsed": "false"
    },
@@ -505,7 +1707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 65,
    "metadata": {
     "Collapsed": "false"
    },
@@ -531,7 +1733,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 66,
    "metadata": {
     "Collapsed": "false"
    },
@@ -553,7 +1755,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "metadata": {
     "Collapsed": "false"
    },
@@ -565,7 +1767,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 68,
    "metadata": {
     "Collapsed": "false"
    },
@@ -578,7 +1780,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 69,
    "metadata": {
     "Collapsed": "false"
    },
@@ -593,7 +1795,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 70,
    "metadata": {
     "Collapsed": "false"
    },
@@ -604,7 +1806,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 71,
    "metadata": {
     "Collapsed": "false"
    },
@@ -618,12 +1820,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 72,
    "metadata": {
     "Collapsed": "false",
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Selected proteins # 219 of a total of # 304!\n"
+     ]
+    }
+   ],
    "source": [
     "CV_COEFFICIENT = 0.3\n",
     "cv_selected = proteins_cv < CV_COEFFICIENT\n",
@@ -632,7 +1842,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 73,
    "metadata": {
     "Collapsed": "false"
    },
@@ -650,7 +1860,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 74,
    "metadata": {
     "Collapsed": "false"
    },
@@ -664,9 +1874,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 75,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A maximum of 219 proteins in 599 samples can be used for proteomic models\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"A maximum of {1} proteins in {0} samples can be used for proteomic models\".format(*data_proteomics.shape))"
    ]
@@ -680,7 +1898,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 76,
    "metadata": {
     "Collapsed": "false"
    },
@@ -692,7 +1910,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 77,
    "metadata": {
     "Collapsed": "false"
    },
@@ -720,11 +1938,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 78,
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Proportion protein has to be shared between samples: 0.6\n",
+      "Minimum number of protein in single sample: 200\n",
+      "Maximum coefficient of variation (CV) for protein intensities: 0.3\n",
+      "Logarithm employed for transformation: <ufunc 'log2'>\n",
+      "Imputation: Mean-Shift: 1.8\n",
+      "Imputation: Std-Dev. shrinkage: 0.3\n"
+     ]
+    }
+   ],
    "source": [
     "summary_protein_preprocessing = [(\"Proportion protein has to be shared between samples\" , DATA_COMPLETENESS),\n",
     "                                 (\"Minimum number of protein in single sample\", MIN_N_PROTEIN_GROUPS),\n",
@@ -752,24 +1983,104 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 79,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Gene names</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Protein ID</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A0A024R6I7</th>\n",
+       "      <td>SERPINA1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A0A075B6I0</th>\n",
+       "      <td>IGLV8-61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A0A075B6J9</th>\n",
+       "      <td>IGLV2-18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A0A075B6R9</th>\n",
+       "      <td>IGKV2D-24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A0A075B6S2</th>\n",
+       "      <td>IGKV2D-29</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Gene names\n",
+       "Protein ID           \n",
+       "A0A024R6I7   SERPINA1\n",
+       "A0A075B6I0   IGLV8-61\n",
+       "A0A075B6J9   IGLV2-18\n",
+       "A0A075B6R9  IGKV2D-24\n",
+       "A0A075B6S2  IGKV2D-29"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "key_ProteinID = pd.read_csv(os.path.join(FOLDER_DATA_RAW, 'ID_matching_key.csv'), \n",
-    "                            index_col=\"Protein ID\").drop(\"Unnamed: 0\", axis=1)\n",
+    "                            index_col=\"Protein ID\")\n",
     "key_ProteinID.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 80,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Gene names    IGKV2D-24\n",
+       "Name: A0A075B6R9, dtype: object"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "key_ProteinID.loc['A0A075B6R9']"
    ]
@@ -783,16 +2094,85 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 81,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Protein ID</th>\n",
+       "      <th>Gene names</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[A0A024R6I7]</td>\n",
+       "      <td>[SERPINA1]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[A0A075B6I0]</td>\n",
+       "      <td>[IGLV8-61]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[A0A075B6J9]</td>\n",
+       "      <td>[IGLV2-18]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[A0A075B6R9, A0A0C4DH68]</td>\n",
+       "      <td>[IGKV2D-24, IGKV2-24]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[A0A075B6S2, A2NJV5]</td>\n",
+       "      <td>[IGKV2D-29, IGKV2-29]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Protein ID             Gene names\n",
+       "0              [A0A024R6I7]             [SERPINA1]\n",
+       "1              [A0A075B6I0]             [IGLV8-61]\n",
+       "2              [A0A075B6J9]             [IGLV2-18]\n",
+       "3  [A0A075B6R9, A0A0C4DH68]  [IGKV2D-24, IGKV2-24]\n",
+       "4      [A0A075B6S2, A2NJV5]  [IGKV2D-29, IGKV2-29]"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ids_.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -811,7 +2191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 83,
    "metadata": {
     "Collapsed": "false"
    },
@@ -823,7 +2203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 84,
    "metadata": {
     "Collapsed": "false"
    },
@@ -840,11 +2220,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 85,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Select clinical markers\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "997dc7b8e15c4f7a96880d75b6b52191",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(SelectMultiple(options=('age', 'kleiner', 'cpa', 'nas', 'nas_inflam', 'nas_portinflam', 'nas_lo…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "w_cols_cli = widgets.SelectMultiple(options=list(data_cli.columns))\n",
     "\n",
@@ -887,11 +2289,114 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 86,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>elf</th>\n",
+       "      <th>ft</th>\n",
+       "      <th>fib4</th>\n",
+       "      <th>apri</th>\n",
+       "      <th>forns</th>\n",
+       "      <th>p3np</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>kleiner</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.0</th>\n",
+       "      <td>35</td>\n",
+       "      <td>33</td>\n",
+       "      <td>36</td>\n",
+       "      <td>36</td>\n",
+       "      <td>36</td>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.0</th>\n",
+       "      <td>120</td>\n",
+       "      <td>93</td>\n",
+       "      <td>120</td>\n",
+       "      <td>121</td>\n",
+       "      <td>123</td>\n",
+       "      <td>113</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2.0</th>\n",
+       "      <td>104</td>\n",
+       "      <td>76</td>\n",
+       "      <td>104</td>\n",
+       "      <td>104</td>\n",
+       "      <td>103</td>\n",
+       "      <td>95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3.0</th>\n",
+       "      <td>27</td>\n",
+       "      <td>17</td>\n",
+       "      <td>26</td>\n",
+       "      <td>26</td>\n",
+       "      <td>27</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.0</th>\n",
+       "      <td>63</td>\n",
+       "      <td>49</td>\n",
+       "      <td>66</td>\n",
+       "      <td>66</td>\n",
+       "      <td>67</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         elf  ft  fib4  apri  forns  p3np\n",
+       "kleiner                                  \n",
+       "0.0       35  33    36    36     36    35\n",
+       "1.0      120  93   120   121    123   113\n",
+       "2.0      104  76   104   104    103    95\n",
+       "3.0       27  17    26    26     27    22\n",
+       "4.0       63  49    66    66     67    54"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#SOTA_fibrosis = ['te', 'swe', 'elf', 'ft', 'fib4', 'apri', 'forns', 'p3np']\n",
     "SOTA_fibrosis = ['elf', 'ft', 'fib4', 'apri', 'forns', 'p3np']\n",
@@ -900,7 +2405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 87,
    "metadata": {
     "Collapsed": "false"
    },
@@ -915,22 +2420,265 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 88,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>nas_steatosis_ordinal</th>\n",
+       "      <th>nas_inflam</th>\n",
+       "      <th>kleiner</th>\n",
+       "      <th>fib4</th>\n",
+       "      <th>elf</th>\n",
+       "      <th>ft</th>\n",
+       "      <th>te</th>\n",
+       "      <th>swe</th>\n",
+       "      <th>aar</th>\n",
+       "      <th>ast</th>\n",
+       "      <th>apri</th>\n",
+       "      <th>forns</th>\n",
+       "      <th>m30</th>\n",
+       "      <th>m65</th>\n",
+       "      <th>meld</th>\n",
+       "      <th>p3np</th>\n",
+       "      <th>timp1</th>\n",
+       "      <th>cap</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>group2</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>ALD</th>\n",
+       "      <td>352</td>\n",
+       "      <td>352</td>\n",
+       "      <td>360</td>\n",
+       "      <td>353</td>\n",
+       "      <td>350</td>\n",
+       "      <td>268</td>\n",
+       "      <td>342</td>\n",
+       "      <td>332</td>\n",
+       "      <td>353</td>\n",
+       "      <td>354</td>\n",
+       "      <td>354</td>\n",
+       "      <td>357</td>\n",
+       "      <td>268</td>\n",
+       "      <td>266</td>\n",
+       "      <td>360</td>\n",
+       "      <td>320</td>\n",
+       "      <td>320</td>\n",
+       "      <td>206</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HP</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>136</td>\n",
+       "      <td>136</td>\n",
+       "      <td>0</td>\n",
+       "      <td>119</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>136</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>133</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        nas_steatosis_ordinal  nas_inflam  kleiner  fib4  elf   ft   te  swe  \\\n",
+       "group2                                                                         \n",
+       "ALD                       352         352      360   353  350  268  342  332   \n",
+       "HP                          0           0        0     0    0    0  136  136   \n",
+       "\n",
+       "        aar  ast  apri  forns  m30  m65  meld  p3np  timp1  cap  \n",
+       "group2                                                           \n",
+       "ALD     353  354   354    357  268  266   360   320    320  206  \n",
+       "HP        0  119     0      0    0    0   136     0      0  133  "
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "data_cli.groupby('group2')[FEATURES_ML].count()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 89,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>te</th>\n",
+       "      <th>swe</th>\n",
+       "      <th>elf</th>\n",
+       "      <th>ft</th>\n",
+       "      <th>fib4</th>\n",
+       "      <th>apri</th>\n",
+       "      <th>forns</th>\n",
+       "      <th>p3np</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>kleiner</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.0</th>\n",
+       "      <td>4.40</td>\n",
+       "      <td>5.750000</td>\n",
+       "      <td>8.30</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>1.070</td>\n",
+       "      <td>0.270</td>\n",
+       "      <td>3.775</td>\n",
+       "      <td>7.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.0</th>\n",
+       "      <td>6.00</td>\n",
+       "      <td>6.700000</td>\n",
+       "      <td>8.80</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>1.195</td>\n",
+       "      <td>0.270</td>\n",
+       "      <td>4.470</td>\n",
+       "      <td>7.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2.0</th>\n",
+       "      <td>9.00</td>\n",
+       "      <td>9.300000</td>\n",
+       "      <td>9.55</td>\n",
+       "      <td>0.34</td>\n",
+       "      <td>1.655</td>\n",
+       "      <td>0.440</td>\n",
+       "      <td>5.820</td>\n",
+       "      <td>9.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3.0</th>\n",
+       "      <td>21.60</td>\n",
+       "      <td>16.900000</td>\n",
+       "      <td>10.60</td>\n",
+       "      <td>0.63</td>\n",
+       "      <td>2.080</td>\n",
+       "      <td>0.485</td>\n",
+       "      <td>7.020</td>\n",
+       "      <td>13.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.0</th>\n",
+       "      <td>40.75</td>\n",
+       "      <td>28.400001</td>\n",
+       "      <td>11.80</td>\n",
+       "      <td>0.78</td>\n",
+       "      <td>4.000</td>\n",
+       "      <td>0.860</td>\n",
+       "      <td>8.150</td>\n",
+       "      <td>22.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            te        swe    elf    ft   fib4   apri  forns  p3np\n",
+       "kleiner                                                          \n",
+       "0.0       4.40   5.750000   8.30  0.10  1.070  0.270  3.775   7.2\n",
+       "1.0       6.00   6.700000   8.80  0.12  1.195  0.270  4.470   7.2\n",
+       "2.0       9.00   9.300000   9.55  0.34  1.655  0.440  5.820   9.9\n",
+       "3.0      21.60  16.900000  10.60  0.63  2.080  0.485  7.020  13.3\n",
+       "4.0      40.75  28.400001  11.80  0.78  4.000  0.860  8.150  22.4"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "SOTA_fibrosis = ['te', 'swe', 'elf', 'ft', 'fib4', 'apri', 'forns', 'p3np']\n",
     "data_cli.groupby('kleiner')[SOTA_fibrosis].median()"
@@ -945,9 +2693,105 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 90,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>bmi</th>\n",
+       "      <th>gender_num</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>497.000000</td>\n",
+       "      <td>487.000000</td>\n",
+       "      <td>497.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>54.953722</td>\n",
+       "      <td>27.194539</td>\n",
+       "      <td>0.726358</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>10.433153</td>\n",
+       "      <td>5.180561</td>\n",
+       "      <td>0.446277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>13.737373</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>48.000000</td>\n",
+       "      <td>23.849999</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>56.000000</td>\n",
+       "      <td>26.799999</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>30.286856</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>75.000000</td>\n",
+       "      <td>52.099998</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              age         bmi  gender_num\n",
+       "count  497.000000  487.000000  497.000000\n",
+       "mean    54.953722   27.194539    0.726358\n",
+       "std     10.433153    5.180561    0.446277\n",
+       "min     19.000000   13.737373    0.000000\n",
+       "25%     48.000000   23.849999    0.000000\n",
+       "50%     56.000000   26.799999    1.000000\n",
+       "75%     62.000000   30.286856    1.000000\n",
+       "max     75.000000   52.099998    1.000000"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "demographics = data_cli[['age', 'bmi', 'gender_num']] # 1 is male\n",
     "demographics.describe()"
@@ -955,9 +2799,84 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 91,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>gender_num</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sample ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Plate6_G11</th>\n",
+       "      <td>64</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plate1_F7</th>\n",
+       "      <td>74</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plate6_D2</th>\n",
+       "      <td>71</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plate6_C5</th>\n",
+       "      <td>53</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plate4_F8</th>\n",
+       "      <td>63</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            age  gender_num\n",
+       "Sample ID                  \n",
+       "Plate6_G11   64           1\n",
+       "Plate1_F7    74           1\n",
+       "Plate6_D2    71           1\n",
+       "Plate6_C5    53           1\n",
+       "Plate4_F8    63           1"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "SELECTED_DEMOGRAPHICS = ['age', 'gender_num']\n",
     "data_cli[SELECTED_DEMOGRAPHICS].head()"
@@ -974,7 +2893,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 92,
    "metadata": {
     "Collapsed": "false"
    },
@@ -987,11 +2906,107 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 93,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>kleiner</th>\n",
+       "      <th>nas_steatosis_ordinal</th>\n",
+       "      <th>nas_inflam</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>360.000000</td>\n",
+       "      <td>352.000000</td>\n",
+       "      <td>352.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1.902778</td>\n",
+       "      <td>0.982955</td>\n",
+       "      <td>1.855114</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.248506</td>\n",
+       "      <td>1.045813</td>\n",
+       "      <td>1.467221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          kleiner  nas_steatosis_ordinal  nas_inflam\n",
+       "count  360.000000             352.000000  352.000000\n",
+       "mean     1.902778               0.982955    1.855114\n",
+       "std      1.248506               1.045813    1.467221\n",
+       "min      0.000000               0.000000    0.000000\n",
+       "25%      1.000000               0.000000    1.000000\n",
+       "50%      2.000000               1.000000    2.000000\n",
+       "75%      3.000000               2.000000    3.000000\n",
+       "max      4.000000               3.000000    5.000000"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "TARGETS = ['kleiner', 'nas_steatosis_ordinal', 'nas_inflam']\n",
     "Y = data_cli[TARGETS]\n",
@@ -1000,9 +3015,98 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 94,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>kleiner</th>\n",
+       "      <th>nas_steatosis_ordinal</th>\n",
+       "      <th>nas_inflam</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.0</th>\n",
+       "      <td>36.0</td>\n",
+       "      <td>156.0</td>\n",
+       "      <td>72.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.0</th>\n",
+       "      <td>124.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>91.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2.0</th>\n",
+       "      <td>106.0</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>82.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3.0</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>53.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.0</th>\n",
+       "      <td>67.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5.0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>23.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total</th>\n",
+       "      <td>360.0</td>\n",
+       "      <td>352.0</td>\n",
+       "      <td>352.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       kleiner  nas_steatosis_ordinal  nas_inflam\n",
+       "0.0       36.0                  156.0        72.0\n",
+       "1.0      124.0                   85.0        91.0\n",
+       "2.0      106.0                   72.0        82.0\n",
+       "3.0       27.0                   39.0        53.0\n",
+       "4.0       67.0                    NaN        31.0\n",
+       "5.0        NaN                    NaN        23.0\n",
+       "Total    360.0                  352.0       352.0"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from src.pandas import combine_value_counts\n",
     "#combcombine_value_counts??\n",
@@ -1040,7 +3144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 95,
    "metadata": {
     "Collapsed": "false"
    },
@@ -1066,11 +3170,76 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 96,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>kleiner&gt;=2</th>\n",
+       "      <th>kleiner&gt;=3</th>\n",
+       "      <th>steatosis&gt;=1</th>\n",
+       "      <th>inflamation&gt;=2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>160</td>\n",
+       "      <td>266</td>\n",
+       "      <td>156</td>\n",
+       "      <td>163</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>200</td>\n",
+       "      <td>94</td>\n",
+       "      <td>196</td>\n",
+       "      <td>189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total</th>\n",
+       "      <td>360</td>\n",
+       "      <td>360</td>\n",
+       "      <td>352</td>\n",
+       "      <td>352</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       kleiner>=2  kleiner>=3  steatosis>=1  inflamation>=2\n",
+       "0             160         266           156             163\n",
+       "1             200          94           196             189\n",
+       "total         360         360           352             352"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "freq_targets = pd.DataFrame(\n",
     "    {'kleiner>=2': kleiner_ge_2.value_counts(dropna=False, sort=False),\n",
@@ -1101,11 +3270,188 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 97,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>F2</th>\n",
+       "      <th>F3</th>\n",
+       "      <th>INFL2</th>\n",
+       "      <th>steatosis</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>marker</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>te</th>\n",
+       "      <td>7.00</td>\n",
+       "      <td>15.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>swe</th>\n",
+       "      <td>8.60</td>\n",
+       "      <td>16.40</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>elf</th>\n",
+       "      <td>7.70</td>\n",
+       "      <td>10.50</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ft</th>\n",
+       "      <td>0.48</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fib4</th>\n",
+       "      <td>1.45</td>\n",
+       "      <td>3.25</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>apri</th>\n",
+       "      <td>0.50</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>forns</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.80</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p3np</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>m30</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>m65</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>aar</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>proc3</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cap</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>290.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alt</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ast</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>m30m65_ratio</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                F2     F3  INFL2  steatosis\n",
+       "marker                                     \n",
+       "te            7.00  15.00    NaN        NaN\n",
+       "swe           8.60  16.40    NaN        NaN\n",
+       "elf           7.70  10.50    NaN        NaN\n",
+       "ft            0.48   0.58    NaN        NaN\n",
+       "fib4          1.45   3.25    NaN        NaN\n",
+       "apri          0.50   1.00    NaN        NaN\n",
+       "forns          NaN   6.80    NaN        NaN\n",
+       "p3np           NaN    NaN    NaN        NaN\n",
+       "m30            NaN    NaN    NaN        NaN\n",
+       "m65            NaN    NaN    NaN        NaN\n",
+       "aar            NaN    NaN    2.0        NaN\n",
+       "proc3          NaN    NaN    NaN        NaN\n",
+       "cap            NaN    NaN    NaN      290.0\n",
+       "alt            NaN    NaN    NaN        NaN\n",
+       "ast            NaN    NaN    NaN        NaN\n",
+       "m30m65_ratio   NaN    NaN    NaN        NaN"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "file_cutoff_clinic = os.path.join(FOLDER_DATA_RAW, \"clinical_marker_test_cut-offs.xlsx\")\n",
     "cutoffs_clinic = pd.read_excel(file_cutoff_clinic, sheet_name=\"cutoffs\", index_col='marker')\n",
@@ -1114,11 +3460,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 98,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "proc3: Missing in clinics data.\n"
+     ]
+    }
+   ],
    "source": [
     "markers_to_drop = []\n",
     "for marker in cutoffs_clinic.index:\n",
@@ -1139,11 +3493,180 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 99,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>F2</th>\n",
+       "      <th>F3</th>\n",
+       "      <th>I2</th>\n",
+       "      <th>S1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>marker</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>te</th>\n",
+       "      <td>7.00</td>\n",
+       "      <td>15.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>swe</th>\n",
+       "      <td>8.60</td>\n",
+       "      <td>16.40</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>elf</th>\n",
+       "      <td>7.70</td>\n",
+       "      <td>10.50</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ft</th>\n",
+       "      <td>0.48</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fib4</th>\n",
+       "      <td>1.45</td>\n",
+       "      <td>3.25</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>apri</th>\n",
+       "      <td>0.50</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>forns</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.80</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p3np</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>m30</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>m65</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>aar</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cap</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>290.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alt</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ast</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>m30m65_ratio</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                F2     F3   I2     S1\n",
+       "marker                               \n",
+       "te            7.00  15.00  NaN    NaN\n",
+       "swe           8.60  16.40  NaN    NaN\n",
+       "elf           7.70  10.50  NaN    NaN\n",
+       "ft            0.48   0.58  NaN    NaN\n",
+       "fib4          1.45   3.25  NaN    NaN\n",
+       "apri          0.50   1.00  NaN    NaN\n",
+       "forns          NaN   6.80  NaN    NaN\n",
+       "p3np           NaN    NaN  NaN    NaN\n",
+       "m30            NaN    NaN  NaN    NaN\n",
+       "m65            NaN    NaN  NaN    NaN\n",
+       "aar            NaN    NaN  2.0    NaN\n",
+       "cap            NaN    NaN  NaN  290.0\n",
+       "alt            NaN    NaN  NaN    NaN\n",
+       "ast            NaN    NaN  NaN    NaN\n",
+       "m30m65_ratio   NaN    NaN  NaN    NaN"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "if markers_to_drop:\n",
     "    cutoffs_clinic.drop(labels=markers_to_drop, inplace=True)\n",
@@ -1162,11 +3685,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 100,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'te': 7.0, 'swe': 8.6, 'elf': 7.7, 'ft': 0.48, 'fib4': 1.45, 'apri': 0.5}"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cutoffs_clinic['F2'].dropna().to_dict()"
    ]
@@ -1182,11 +3716,128 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 101,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>te</th>\n",
+       "      <th>swe</th>\n",
+       "      <th>elf</th>\n",
+       "      <th>ft</th>\n",
+       "      <th>fib4</th>\n",
+       "      <th>apri</th>\n",
+       "      <th>forns</th>\n",
+       "      <th>p3np</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>kleiner</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.0</th>\n",
+       "      <td>4.40</td>\n",
+       "      <td>5.750000</td>\n",
+       "      <td>8.30</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>1.070</td>\n",
+       "      <td>0.270</td>\n",
+       "      <td>3.775</td>\n",
+       "      <td>7.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.0</th>\n",
+       "      <td>6.00</td>\n",
+       "      <td>6.700000</td>\n",
+       "      <td>8.80</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>1.195</td>\n",
+       "      <td>0.270</td>\n",
+       "      <td>4.470</td>\n",
+       "      <td>7.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2.0</th>\n",
+       "      <td>9.00</td>\n",
+       "      <td>9.300000</td>\n",
+       "      <td>9.55</td>\n",
+       "      <td>0.34</td>\n",
+       "      <td>1.655</td>\n",
+       "      <td>0.440</td>\n",
+       "      <td>5.820</td>\n",
+       "      <td>9.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3.0</th>\n",
+       "      <td>21.60</td>\n",
+       "      <td>16.900000</td>\n",
+       "      <td>10.60</td>\n",
+       "      <td>0.63</td>\n",
+       "      <td>2.080</td>\n",
+       "      <td>0.485</td>\n",
+       "      <td>7.020</td>\n",
+       "      <td>13.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.0</th>\n",
+       "      <td>40.75</td>\n",
+       "      <td>28.400001</td>\n",
+       "      <td>11.80</td>\n",
+       "      <td>0.78</td>\n",
+       "      <td>4.000</td>\n",
+       "      <td>0.860</td>\n",
+       "      <td>8.150</td>\n",
+       "      <td>22.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            te        swe    elf    ft   fib4   apri  forns  p3np\n",
+       "kleiner                                                          \n",
+       "0.0       4.40   5.750000   8.30  0.10  1.070  0.270  3.775   7.2\n",
+       "1.0       6.00   6.700000   8.80  0.12  1.195  0.270  4.470   7.2\n",
+       "2.0       9.00   9.300000   9.55  0.34  1.655  0.440  5.820   9.9\n",
+       "3.0      21.60  16.900000  10.60  0.63  2.080  0.485  7.020  13.3\n",
+       "4.0      40.75  28.400001  11.80  0.78  4.000  0.860  8.150  22.4"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "SOTA_fibrosis = ['te', 'swe', 'elf', 'ft', 'fib4', 'apri', 'forns', 'p3np']\n",
     "data_cli.groupby('kleiner')[SOTA_fibrosis].median()"
@@ -1207,11 +3858,235 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 102,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>te</th>\n",
+       "      <th>swe</th>\n",
+       "      <th>elf</th>\n",
+       "      <th>ft</th>\n",
+       "      <th>fib4</th>\n",
+       "      <th>apri</th>\n",
+       "      <th>forns</th>\n",
+       "      <th>p3np</th>\n",
+       "      <th>m30</th>\n",
+       "      <th>m65</th>\n",
+       "      <th>aar</th>\n",
+       "      <th>cap</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>ast</th>\n",
+       "      <th>m30m65_ratio</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>478.000000</td>\n",
+       "      <td>468.000000</td>\n",
+       "      <td>350.000000</td>\n",
+       "      <td>268.000000</td>\n",
+       "      <td>353.000000</td>\n",
+       "      <td>354.000000</td>\n",
+       "      <td>357.000000</td>\n",
+       "      <td>320.000000</td>\n",
+       "      <td>268.000000</td>\n",
+       "      <td>266.000000</td>\n",
+       "      <td>353.000000</td>\n",
+       "      <td>339.000000</td>\n",
+       "      <td>497.000000</td>\n",
+       "      <td>473.000000</td>\n",
+       "      <td>266.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>12.909623</td>\n",
+       "      <td>11.406838</td>\n",
+       "      <td>9.776571</td>\n",
+       "      <td>0.347351</td>\n",
+       "      <td>2.534051</td>\n",
+       "      <td>0.702994</td>\n",
+       "      <td>5.631933</td>\n",
+       "      <td>13.262187</td>\n",
+       "      <td>233.677756</td>\n",
+       "      <td>685.670989</td>\n",
+       "      <td>1.370283</td>\n",
+       "      <td>274.348083</td>\n",
+       "      <td>37.961771</td>\n",
+       "      <td>44.262156</td>\n",
+       "      <td>0.386805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>16.416191</td>\n",
+       "      <td>10.564157</td>\n",
+       "      <td>1.508532</td>\n",
+       "      <td>0.286632</td>\n",
+       "      <td>3.023615</td>\n",
+       "      <td>0.990803</td>\n",
+       "      <td>2.390873</td>\n",
+       "      <td>12.424449</td>\n",
+       "      <td>297.008804</td>\n",
+       "      <td>850.377366</td>\n",
+       "      <td>0.764884</td>\n",
+       "      <td>63.001900</td>\n",
+       "      <td>32.346981</td>\n",
+       "      <td>36.985152</td>\n",
+       "      <td>0.207271</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>2.100000</td>\n",
+       "      <td>2.100000</td>\n",
+       "      <td>6.900000</td>\n",
+       "      <td>0.020000</td>\n",
+       "      <td>0.270000</td>\n",
+       "      <td>0.080000</td>\n",
+       "      <td>-1.540000</td>\n",
+       "      <td>1.500000</td>\n",
+       "      <td>2.126260</td>\n",
+       "      <td>82.780701</td>\n",
+       "      <td>0.090000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>12.000000</td>\n",
+       "      <td>0.020000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>4.500000</td>\n",
+       "      <td>5.600000</td>\n",
+       "      <td>8.700000</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>0.950000</td>\n",
+       "      <td>0.240000</td>\n",
+       "      <td>4.020000</td>\n",
+       "      <td>6.775000</td>\n",
+       "      <td>95.456402</td>\n",
+       "      <td>271.321750</td>\n",
+       "      <td>0.880000</td>\n",
+       "      <td>228.000000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>0.262500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>6.200000</td>\n",
+       "      <td>7.050000</td>\n",
+       "      <td>9.400000</td>\n",
+       "      <td>0.245000</td>\n",
+       "      <td>1.570000</td>\n",
+       "      <td>0.405000</td>\n",
+       "      <td>5.520000</td>\n",
+       "      <td>8.950000</td>\n",
+       "      <td>153.139500</td>\n",
+       "      <td>440.100005</td>\n",
+       "      <td>1.130000</td>\n",
+       "      <td>277.000000</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>32.000000</td>\n",
+       "      <td>0.345000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>10.900000</td>\n",
+       "      <td>11.700000</td>\n",
+       "      <td>10.700000</td>\n",
+       "      <td>0.542500</td>\n",
+       "      <td>2.890000</td>\n",
+       "      <td>0.770000</td>\n",
+       "      <td>7.140000</td>\n",
+       "      <td>14.425000</td>\n",
+       "      <td>261.165740</td>\n",
+       "      <td>835.044495</td>\n",
+       "      <td>1.640000</td>\n",
+       "      <td>321.000000</td>\n",
+       "      <td>45.000000</td>\n",
+       "      <td>48.000000</td>\n",
+       "      <td>0.470000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>75.000000</td>\n",
+       "      <td>75.599998</td>\n",
+       "      <td>14.800000</td>\n",
+       "      <td>0.980000</td>\n",
+       "      <td>32.700001</td>\n",
+       "      <td>11.630000</td>\n",
+       "      <td>12.580000</td>\n",
+       "      <td>112.500000</td>\n",
+       "      <td>3816.990000</td>\n",
+       "      <td>10015.900000</td>\n",
+       "      <td>4.710000</td>\n",
+       "      <td>400.000000</td>\n",
+       "      <td>387.000000</td>\n",
+       "      <td>331.000000</td>\n",
+       "      <td>1.470000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               te         swe         elf          ft        fib4        apri  \\\n",
+       "count  478.000000  468.000000  350.000000  268.000000  353.000000  354.000000   \n",
+       "mean    12.909623   11.406838    9.776571    0.347351    2.534051    0.702994   \n",
+       "std     16.416191   10.564157    1.508532    0.286632    3.023615    0.990803   \n",
+       "min      2.100000    2.100000    6.900000    0.020000    0.270000    0.080000   \n",
+       "25%      4.500000    5.600000    8.700000    0.100000    0.950000    0.240000   \n",
+       "50%      6.200000    7.050000    9.400000    0.245000    1.570000    0.405000   \n",
+       "75%     10.900000   11.700000   10.700000    0.542500    2.890000    0.770000   \n",
+       "max     75.000000   75.599998   14.800000    0.980000   32.700001   11.630000   \n",
+       "\n",
+       "            forns        p3np          m30           m65         aar  \\\n",
+       "count  357.000000  320.000000   268.000000    266.000000  353.000000   \n",
+       "mean     5.631933   13.262187   233.677756    685.670989    1.370283   \n",
+       "std      2.390873   12.424449   297.008804    850.377366    0.764884   \n",
+       "min     -1.540000    1.500000     2.126260     82.780701    0.090000   \n",
+       "25%      4.020000    6.775000    95.456402    271.321750    0.880000   \n",
+       "50%      5.520000    8.950000   153.139500    440.100005    1.130000   \n",
+       "75%      7.140000   14.425000   261.165740    835.044495    1.640000   \n",
+       "max     12.580000  112.500000  3816.990000  10015.900000    4.710000   \n",
+       "\n",
+       "              cap         alt         ast  m30m65_ratio  \n",
+       "count  339.000000  497.000000  473.000000    266.000000  \n",
+       "mean   274.348083   37.961771   44.262156      0.386805  \n",
+       "std     63.001900   32.346981   36.985152      0.207271  \n",
+       "min    100.000000    7.000000   12.000000      0.020000  \n",
+       "25%    228.000000   21.000000   24.000000      0.262500  \n",
+       "50%    277.000000   28.000000   32.000000      0.345000  \n",
+       "75%    321.000000   45.000000   48.000000      0.470000  \n",
+       "max    400.000000  387.000000  331.000000      1.470000  "
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# FEATURES_CLINIC = ['ggt', 'alt', 'ast', 'alk', 'mcv', 'iga', 'igg', 'leu', 'glc']\n",
     "FEATURES_CLINIC = cutoffs_clinic.index\n",
@@ -1227,9 +4102,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 103,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No. of samples without target variable: 137 \n"
+     ]
+    }
+   ],
    "source": [
     "patient_ids_w_target = data_cli[TARGETS].dropna(how='all').index\n",
     "print(f\"No. of samples without target variable: {len(data_cli) -len(patient_ids_w_target)} \")"
@@ -1244,9 +4127,288 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 104,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>alt</th>\n",
+       "      <th>kleiner</th>\n",
+       "      <th>gender_num</th>\n",
+       "      <th>age</th>\n",
+       "      <th>forns</th>\n",
+       "      <th>apri</th>\n",
+       "      <th>ast</th>\n",
+       "      <th>aar</th>\n",
+       "      <th>nas_steatosis_ordinal</th>\n",
+       "      <th>nas_inflam</th>\n",
+       "      <th>fib4</th>\n",
+       "      <th>elf</th>\n",
+       "      <th>te</th>\n",
+       "      <th>swe</th>\n",
+       "      <th>p3np</th>\n",
+       "      <th>m30</th>\n",
+       "      <th>ft</th>\n",
+       "      <th>m65</th>\n",
+       "      <th>m30m65_ratio</th>\n",
+       "      <th>cap</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>360.000000</td>\n",
+       "      <td>360.000000</td>\n",
+       "      <td>360.000000</td>\n",
+       "      <td>360.000000</td>\n",
+       "      <td>356.000000</td>\n",
+       "      <td>353.000000</td>\n",
+       "      <td>353.000000</td>\n",
+       "      <td>352.000000</td>\n",
+       "      <td>352.000000</td>\n",
+       "      <td>352.000000</td>\n",
+       "      <td>352.000000</td>\n",
+       "      <td>349.000000</td>\n",
+       "      <td>341.000000</td>\n",
+       "      <td>331.000000</td>\n",
+       "      <td>319.000000</td>\n",
+       "      <td>268.000000</td>\n",
+       "      <td>268.000000</td>\n",
+       "      <td>266.000000</td>\n",
+       "      <td>266.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>42.552778</td>\n",
+       "      <td>1.902778</td>\n",
+       "      <td>0.763889</td>\n",
+       "      <td>55.536111</td>\n",
+       "      <td>5.630197</td>\n",
+       "      <td>0.703683</td>\n",
+       "      <td>50.036827</td>\n",
+       "      <td>1.367727</td>\n",
+       "      <td>0.982955</td>\n",
+       "      <td>1.855114</td>\n",
+       "      <td>2.534773</td>\n",
+       "      <td>9.777650</td>\n",
+       "      <td>16.260704</td>\n",
+       "      <td>13.842900</td>\n",
+       "      <td>13.279937</td>\n",
+       "      <td>233.677756</td>\n",
+       "      <td>0.347351</td>\n",
+       "      <td>685.670989</td>\n",
+       "      <td>0.386805</td>\n",
+       "      <td>287.107317</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>36.396417</td>\n",
+       "      <td>1.248506</td>\n",
+       "      <td>0.425282</td>\n",
+       "      <td>10.684319</td>\n",
+       "      <td>2.394013</td>\n",
+       "      <td>0.992125</td>\n",
+       "      <td>40.749948</td>\n",
+       "      <td>0.764461</td>\n",
+       "      <td>1.045813</td>\n",
+       "      <td>1.467221</td>\n",
+       "      <td>3.027889</td>\n",
+       "      <td>1.510562</td>\n",
+       "      <td>18.386198</td>\n",
+       "      <td>11.711420</td>\n",
+       "      <td>12.439905</td>\n",
+       "      <td>297.008804</td>\n",
+       "      <td>0.286632</td>\n",
+       "      <td>850.377366</td>\n",
+       "      <td>0.207271</td>\n",
+       "      <td>63.930402</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>-1.540000</td>\n",
+       "      <td>0.080000</td>\n",
+       "      <td>12.000000</td>\n",
+       "      <td>0.090000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.270000</td>\n",
+       "      <td>6.900000</td>\n",
+       "      <td>2.100000</td>\n",
+       "      <td>2.100000</td>\n",
+       "      <td>1.500000</td>\n",
+       "      <td>2.126260</td>\n",
+       "      <td>0.020000</td>\n",
+       "      <td>82.780701</td>\n",
+       "      <td>0.020000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>22.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>49.000000</td>\n",
+       "      <td>4.012500</td>\n",
+       "      <td>0.240000</td>\n",
+       "      <td>26.000000</td>\n",
+       "      <td>0.877500</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.950000</td>\n",
+       "      <td>8.700000</td>\n",
+       "      <td>5.600000</td>\n",
+       "      <td>6.500000</td>\n",
+       "      <td>6.750000</td>\n",
+       "      <td>95.456402</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>271.321750</td>\n",
+       "      <td>0.262500</td>\n",
+       "      <td>245.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>33.500000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>56.000000</td>\n",
+       "      <td>5.515000</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>1.130000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.570000</td>\n",
+       "      <td>9.400000</td>\n",
+       "      <td>8.700000</td>\n",
+       "      <td>8.700000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>153.139500</td>\n",
+       "      <td>0.245000</td>\n",
+       "      <td>440.100005</td>\n",
+       "      <td>0.345000</td>\n",
+       "      <td>286.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>52.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>63.000000</td>\n",
+       "      <td>7.142500</td>\n",
+       "      <td>0.770000</td>\n",
+       "      <td>57.000000</td>\n",
+       "      <td>1.640000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2.892500</td>\n",
+       "      <td>10.700000</td>\n",
+       "      <td>18.200001</td>\n",
+       "      <td>15.850000</td>\n",
+       "      <td>14.450000</td>\n",
+       "      <td>261.165740</td>\n",
+       "      <td>0.542500</td>\n",
+       "      <td>835.044495</td>\n",
+       "      <td>0.470000</td>\n",
+       "      <td>331.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>387.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>75.000000</td>\n",
+       "      <td>12.580000</td>\n",
+       "      <td>11.630000</td>\n",
+       "      <td>331.000000</td>\n",
+       "      <td>4.710000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>32.700001</td>\n",
+       "      <td>14.800000</td>\n",
+       "      <td>75.000000</td>\n",
+       "      <td>75.599998</td>\n",
+       "      <td>112.500000</td>\n",
+       "      <td>3816.990000</td>\n",
+       "      <td>0.980000</td>\n",
+       "      <td>10015.900000</td>\n",
+       "      <td>1.470000</td>\n",
+       "      <td>400.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              alt     kleiner  gender_num         age       forns        apri  \\\n",
+       "count  360.000000  360.000000  360.000000  360.000000  356.000000  353.000000   \n",
+       "mean    42.552778    1.902778    0.763889   55.536111    5.630197    0.703683   \n",
+       "std     36.396417    1.248506    0.425282   10.684319    2.394013    0.992125   \n",
+       "min      8.000000    0.000000    0.000000   19.000000   -1.540000    0.080000   \n",
+       "25%     22.000000    1.000000    1.000000   49.000000    4.012500    0.240000   \n",
+       "50%     33.500000    2.000000    1.000000   56.000000    5.515000    0.400000   \n",
+       "75%     52.000000    3.000000    1.000000   63.000000    7.142500    0.770000   \n",
+       "max    387.000000    4.000000    1.000000   75.000000   12.580000   11.630000   \n",
+       "\n",
+       "              ast         aar  nas_steatosis_ordinal  nas_inflam        fib4  \\\n",
+       "count  353.000000  352.000000             352.000000  352.000000  352.000000   \n",
+       "mean    50.036827    1.367727               0.982955    1.855114    2.534773   \n",
+       "std     40.749948    0.764461               1.045813    1.467221    3.027889   \n",
+       "min     12.000000    0.090000               0.000000    0.000000    0.270000   \n",
+       "25%     26.000000    0.877500               0.000000    1.000000    0.950000   \n",
+       "50%     37.000000    1.130000               1.000000    2.000000    1.570000   \n",
+       "75%     57.000000    1.640000               2.000000    3.000000    2.892500   \n",
+       "max    331.000000    4.710000               3.000000    5.000000   32.700001   \n",
+       "\n",
+       "              elf          te         swe        p3np          m30  \\\n",
+       "count  349.000000  341.000000  331.000000  319.000000   268.000000   \n",
+       "mean     9.777650   16.260704   13.842900   13.279937   233.677756   \n",
+       "std      1.510562   18.386198   11.711420   12.439905   297.008804   \n",
+       "min      6.900000    2.100000    2.100000    1.500000     2.126260   \n",
+       "25%      8.700000    5.600000    6.500000    6.750000    95.456402   \n",
+       "50%      9.400000    8.700000    8.700000    9.000000   153.139500   \n",
+       "75%     10.700000   18.200001   15.850000   14.450000   261.165740   \n",
+       "max     14.800000   75.000000   75.599998  112.500000  3816.990000   \n",
+       "\n",
+       "               ft           m65  m30m65_ratio         cap  \n",
+       "count  268.000000    266.000000    266.000000  205.000000  \n",
+       "mean     0.347351    685.670989      0.386805  287.107317  \n",
+       "std      0.286632    850.377366      0.207271   63.930402  \n",
+       "min      0.020000     82.780701      0.020000  100.000000  \n",
+       "25%      0.100000    271.321750      0.262500  245.000000  \n",
+       "50%      0.245000    440.100005      0.345000  286.000000  \n",
+       "75%      0.542500    835.044495      0.470000  331.000000  \n",
+       "max      0.980000  10015.900000      1.470000  400.000000  "
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "FEATURES_CLINIC_ALL = list(FEATURES_CLINIC) + SELECTED_DEMOGRAPHICS + TARGETS\n",
     "data_cli.loc[patient_ids_w_target, FEATURES_CLINIC_ALL].describe().sort_values(by=\"count\", ascending=False, axis=1)"
@@ -1254,12 +4416,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 105,
    "metadata": {
     "Collapsed": "false",
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Used features: te, swe, elf, ft, fib4, apri, forns, p3np, m30, m65, aar, cap, alt, ast, m30m65_ratio, age, gender_num, kleiner, nas_steatosis_ordinal, nas_inflam\n"
+     ]
+    }
+   ],
    "source": [
     "def ordered_missing_table(data:pd.DataFrame):\n",
     "    \"\"\"Order dataframe by data completeness (first column has most features) \n",
@@ -1278,9 +4448,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 106,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "kleiner                  360\n",
+       "gender_num               360\n",
+       "age                      360\n",
+       "alt                      360\n",
+       "forns                    356\n",
+       "ast                      353\n",
+       "apri                     353\n",
+       "nas_inflam               352\n",
+       "fib4                     352\n",
+       "aar                      352\n",
+       "nas_steatosis_ordinal    352\n",
+       "elf                      349\n",
+       "te                       341\n",
+       "swe                      331\n",
+       "p3np                     319\n",
+       "m30                      268\n",
+       "ft                       268\n",
+       "m30m65_ratio             266\n",
+       "m65                      266\n",
+       "cap                      205\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "data_cli_missing_table.describe().loc['count'].astype(int)"
    ]
@@ -1294,18 +4495,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 107,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "data_proteomics.isna().any(axis=None)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 108,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 diagnosed patients have no valid proteome measure: Plate4_E3, Plate7_B6\n"
+     ]
+    }
+   ],
    "source": [
     "in_both = data_proteomics.index.intersection(data_cli_missing_table.index)\n",
     "samples_wo_proteomics_data = data_cli_missing_table.index.difference(in_both)\n",
@@ -1317,7 +4537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1327,16 +4547,297 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 110,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>kleiner</th>\n",
+       "      <th>gender_num</th>\n",
+       "      <th>age</th>\n",
+       "      <th>alt</th>\n",
+       "      <th>forns</th>\n",
+       "      <th>ast</th>\n",
+       "      <th>apri</th>\n",
+       "      <th>nas_inflam</th>\n",
+       "      <th>fib4</th>\n",
+       "      <th>aar</th>\n",
+       "      <th>...</th>\n",
+       "      <th>elf</th>\n",
+       "      <th>te</th>\n",
+       "      <th>swe</th>\n",
+       "      <th>p3np</th>\n",
+       "      <th>m30</th>\n",
+       "      <th>ft</th>\n",
+       "      <th>m30m65_ratio</th>\n",
+       "      <th>m65</th>\n",
+       "      <th>cap</th>\n",
+       "      <th>has_prot</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>360.0</td>\n",
+       "      <td>360.0</td>\n",
+       "      <td>360.0</td>\n",
+       "      <td>360.0</td>\n",
+       "      <td>356.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>352.0</td>\n",
+       "      <td>352.0</td>\n",
+       "      <td>352.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>349.0</td>\n",
+       "      <td>341.0</td>\n",
+       "      <td>331.0</td>\n",
+       "      <td>319.0</td>\n",
+       "      <td>268.0</td>\n",
+       "      <td>268.0</td>\n",
+       "      <td>266.0</td>\n",
+       "      <td>266.0</td>\n",
+       "      <td>205.0</td>\n",
+       "      <td>358.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       kleiner  gender_num    age    alt  forns    ast   apri  nas_inflam  \\\n",
+       "count    360.0       360.0  360.0  360.0  356.0  353.0  353.0       352.0   \n",
+       "mean       1.0         1.0    1.0    1.0    1.0    1.0    1.0         1.0   \n",
+       "std        0.0         0.0    0.0    0.0    0.0    0.0    0.0         0.0   \n",
+       "min        1.0         1.0    1.0    1.0    1.0    1.0    1.0         1.0   \n",
+       "25%        1.0         1.0    1.0    1.0    1.0    1.0    1.0         1.0   \n",
+       "50%        1.0         1.0    1.0    1.0    1.0    1.0    1.0         1.0   \n",
+       "75%        1.0         1.0    1.0    1.0    1.0    1.0    1.0         1.0   \n",
+       "max        1.0         1.0    1.0    1.0    1.0    1.0    1.0         1.0   \n",
+       "\n",
+       "        fib4    aar  ...    elf     te    swe   p3np    m30     ft  \\\n",
+       "count  352.0  352.0  ...  349.0  341.0  331.0  319.0  268.0  268.0   \n",
+       "mean     1.0    1.0  ...    1.0    1.0    1.0    1.0    1.0    1.0   \n",
+       "std      0.0    0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+       "min      1.0    1.0  ...    1.0    1.0    1.0    1.0    1.0    1.0   \n",
+       "25%      1.0    1.0  ...    1.0    1.0    1.0    1.0    1.0    1.0   \n",
+       "50%      1.0    1.0  ...    1.0    1.0    1.0    1.0    1.0    1.0   \n",
+       "75%      1.0    1.0  ...    1.0    1.0    1.0    1.0    1.0    1.0   \n",
+       "max      1.0    1.0  ...    1.0    1.0    1.0    1.0    1.0    1.0   \n",
+       "\n",
+       "       m30m65_ratio    m65    cap  has_prot  \n",
+       "count         266.0  266.0  205.0     358.0  \n",
+       "mean            1.0    1.0    1.0       1.0  \n",
+       "std             0.0    0.0    0.0       0.0  \n",
+       "min             1.0    1.0    1.0       1.0  \n",
+       "25%             1.0    1.0    1.0       1.0  \n",
+       "50%             1.0    1.0    1.0       1.0  \n",
+       "75%             1.0    1.0    1.0       1.0  \n",
+       "max             1.0    1.0    1.0       1.0  \n",
+       "\n",
+       "[8 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "data_cli_missing_table.dropna(how='all').describe()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1345,9 +4846,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 112,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "111111111111111111111    115\n",
+       "111111111111111111110    107\n",
+       "111111111111111100001     30\n",
+       "111111111111111000001     23\n",
+       "111111111111110111110     19\n",
+       "111111111111101111110     12\n",
+       "111111111111111100000      6\n",
+       "111111111111011000001      5\n",
+       "111111111111111101001      5\n",
+       "111111111111100111110      4\n",
+       "111111111111111110111      4\n",
+       "111111000011011000001      2\n",
+       "111111111111111111001      2\n",
+       "111111000011111100001      2\n",
+       "111110111111111100001      2\n",
+       "111111111100011000001      2\n",
+       "111111000011111000001      2\n",
+       "111110111111111100000      1\n",
+       "111111111111110000001      1\n",
+       "111111111100110000001      1\n",
+       "111111111100111111110      1\n",
+       "111111111100101111110      1\n",
+       "111111111111100100000      1\n",
+       "111111111111101111111      1\n",
+       "111111111111110100000      1\n",
+       "111111111111111000000      1\n",
+       "111111111111110100001      1\n",
+       "111111110011111111111      1\n",
+       "111101000011011000001      1\n",
+       "111111111100110100001      1\n",
+       "111111111111111110110      1\n",
+       "111111111100111000001      1\n",
+       "111110111111111000001      1\n",
+       "111111111100111100001      1\n",
+       "111101111111011000001      1\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "data_cli_missing_strings = data_cli_missing_table.fillna(value=0)\n",
     "data_cli_missing_strings = data_cli_missing_strings.astype(str)\n",
@@ -1366,7 +4913,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1380,18 +4927,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 114,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "stratifier.value_counts().min()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 115,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "111111111111111111111    121\n",
+       "111111111111111111110    109\n",
+       "111111111111111100001     35\n",
+       "111111111111111000001     26\n",
+       "111111111111110111110     23\n",
+       "111111111111101111110     13\n",
+       "111111111111011000001     10\n",
+       "111111111111111100000      9\n",
+       "111111000011011000001      7\n",
+       "111111111111111101001      7\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "def update_stratifier(stratifier_var:pd.Series, threshold:int=None, verbose:bool=False):\n",
     "    \"\"\"Takes a stratifier variable, and assigns the pattern the less \n",
@@ -1440,7 +5019,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1457,9 +5036,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 117,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Index(['Plate4_E3', 'Plate1_G11', 'Plate5_A2', 'Plate3_A10', 'Plate1_E10',\n",
+       "        'Plate1_E8', 'Plate5_C3', 'Plate4_H11', 'Plate2_C4', 'Plate4_G11',\n",
+       "        ...\n",
+       "        'Plate2_A2', 'Plate2_E6', 'Plate7_C6', 'Plate1_B7', 'Plate6_D11',\n",
+       "        'Plate7_A9', 'Plate2_E3', 'Plate3_E9', 'Plate2_B9', 'Plate3_G1'],\n",
+       "       dtype='object', name='Sample ID', length=288),\n",
+       " Index(['Plate7_B6', 'Plate3_B6', 'Plate6_A10', 'Plate1_F8', 'Plate4_E11',\n",
+       "        'Plate1_D12', 'Plate1_B8', 'Plate3_E12', 'Plate6_C5', 'Plate5_A4',\n",
+       "        'Plate1_D5', 'Plate4_A7', 'Plate3_D6', 'Plate3_H10', 'Plate4_C8',\n",
+       "        'Plate1_H3', 'Plate7_A6', 'Plate5_B10', 'Plate5_F9', 'Plate5_H8',\n",
+       "        'Plate5_B2', 'Plate2_H5', 'Plate4_D4', 'Plate3_B8', 'Plate2_C11',\n",
+       "        'Plate1_G8', 'Plate2_G3', 'Plate5_E12', 'Plate4_F8', 'Plate4_G8',\n",
+       "        'Plate2_G1', 'Plate4_C3', 'Plate6_B9', 'Plate2_A12', 'Plate7_C10',\n",
+       "        'Plate3_F5', 'Plate4_B10', 'Plate4_E9', 'Plate4_F1', 'Plate7_C9',\n",
+       "        'Plate5_G6', 'Plate1_H8', 'Plate3_D3', 'Plate2_B12', 'Plate4_B4',\n",
+       "        'Plate4_H9', 'Plate2_D3', 'Plate7_C11', 'Plate6_D3', 'Plate5_E2',\n",
+       "        'Plate7_A12', 'Plate3_G10', 'Plate1_G12', 'Plate3_E1', 'Plate5_G2',\n",
+       "        'Plate6_G6', 'Plate3_B10', 'Plate1_D10', 'Plate5_E5', 'Plate2_H9',\n",
+       "        'Plate6_G12', 'Plate1_F10', 'Plate1_E3', 'Plate4_B6', 'Plate6_C7',\n",
+       "        'Plate2_G10', 'Plate6_B8', 'Plate6_E4', 'Plate7_C12', 'Plate5_C11',\n",
+       "        'Plate1_D8', 'Plate4_A2'],\n",
+       "       dtype='object', name='Sample ID'))"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cv_train_test_indices = list()\n",
     "for train_indices, test_indices in splits:\n",
@@ -1485,7 +5096,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1549,7 +5160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 119,
    "metadata": {
     "Collapsed": "false"
    },
@@ -1590,21 +5201,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 120,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cutoffs_clinic.loc['te','F2']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 121,
    "metadata": {
     "Collapsed": "false",
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'te': 7.0}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 1.],\n",
+       "       [1., 0.],\n",
+       "       [0., 1.],\n",
+       "       [0., 1.]])"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from src.threshold_classifier import ThresholdClassifier\n",
     "clf_te = ThresholdClassifier(threshold={'te':7.0})\n",
@@ -1625,7 +5268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 122,
    "metadata": {
     "Collapsed": "false"
    },
@@ -1646,7 +5289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 123,
    "metadata": {
     "Collapsed": "false"
    },
@@ -1666,11 +5309,83 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 124,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "As DataFrame:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>pred</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>true</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>280</td>\n",
+       "      <td>123</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9</td>\n",
+       "      <td>85</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "pred    0    1\n",
+       "true          \n",
+       "0     280  123\n",
+       "1       9   85"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plain:\n",
+      " [[280 123]\n",
+      " [  9  85]]\n"
+     ]
+    }
+   ],
    "source": [
     "y_true = data_cli.kleiner > 2.0\n",
     "\n",
@@ -1718,11 +5433,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 125,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['precision', 'recall', 'f1', 'balanced_accuracy', 'roc_auc']"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from sklearn.model_selection import cross_validate\n",
     "from sklearn.metrics import roc_curve\n",
@@ -1751,7 +5477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1772,12 +5498,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 127,
    "metadata": {
     "Collapsed": "false",
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Klassifiers: f2_te, Logistic\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['f2_te', 'Logistic'])"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from src.cross_validation import run_cv_binary_simple, _get_cv_means\n",
     "clf = {**{'f2_te': clf_te}, **clf_sklearn}\n",
@@ -1789,9 +5533,131 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 128,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>variable</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">fit_time</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">score_time</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">test_precision</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">test_recall</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">test_f1</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">test_balanced_accuracy</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">test_roc_auc</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">num_feat</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">n_obs</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>statistics</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Logistic</th>\n",
+       "      <td>0.002592</td>\n",
+       "      <td>0.000222</td>\n",
+       "      <td>0.006597</td>\n",
+       "      <td>0.000105</td>\n",
+       "      <td>0.846000</td>\n",
+       "      <td>0.119711</td>\n",
+       "      <td>0.705</td>\n",
+       "      <td>0.123895</td>\n",
+       "      <td>0.751956</td>\n",
+       "      <td>0.049203</td>\n",
+       "      <td>0.74625</td>\n",
+       "      <td>0.081452</td>\n",
+       "      <td>0.829453</td>\n",
+       "      <td>0.029588</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>360.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f2_te</th>\n",
+       "      <td>0.001459</td>\n",
+       "      <td>0.000230</td>\n",
+       "      <td>0.007805</td>\n",
+       "      <td>0.000453</td>\n",
+       "      <td>0.788256</td>\n",
+       "      <td>0.099734</td>\n",
+       "      <td>0.765</td>\n",
+       "      <td>0.090277</td>\n",
+       "      <td>0.765368</td>\n",
+       "      <td>0.032199</td>\n",
+       "      <td>0.73250</td>\n",
+       "      <td>0.083212</td>\n",
+       "      <td>0.732500</td>\n",
+       "      <td>0.083212</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>360.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "variable    fit_time           score_time           test_precision            \\\n",
+       "statistics      mean       std       mean       std           mean       std   \n",
+       "Logistic    0.002592  0.000222   0.006597  0.000105       0.846000  0.119711   \n",
+       "f2_te       0.001459  0.000230   0.007805  0.000453       0.788256  0.099734   \n",
+       "\n",
+       "variable   test_recall             test_f1           test_balanced_accuracy  \\\n",
+       "statistics        mean       std      mean       std                   mean   \n",
+       "Logistic         0.705  0.123895  0.751956  0.049203                0.74625   \n",
+       "f2_te            0.765  0.090277  0.765368  0.032199                0.73250   \n",
+       "\n",
+       "variable             test_roc_auc           num_feat       n_obs       \n",
+       "statistics       std         mean       std     mean  std   mean  std  \n",
+       "Logistic    0.081452     0.829453  0.029588      1.0  0.0  360.0  0.0  \n",
+       "f2_te       0.083212     0.732500  0.083212      1.0  0.0  360.0  0.0  "
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "_get_cv_means(result_dict).sort_values(('test_f1', 'mean'))"
    ]
@@ -1825,9 +5691,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 129,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Custom cutoff defined by Logistic regressor: 8.80 \n",
+      "Custom cutoff defined by Logistic regressor: 8.61 \n",
+      "Custom cutoff defined by Logistic regressor: 8.89 \n",
+      "Custom cutoff defined by Logistic regressor: 8.91 \n",
+      "Custom cutoff defined by Logistic regressor: 7.58 \n"
+     ]
+    }
+   ],
    "source": [
     "for lr_est in result_dict['Logistic']['estimator']:\n",
     "    # lr_0 = result_dict['Logistic']['estimator'][0]    \n",
@@ -1847,7 +5725,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1857,7 +5735,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1866,7 +5744,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1885,11 +5763,145 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 133,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>variable</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">precision</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">recall</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">f1</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">balanced_accuracy</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">roc_auc</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">num_feat</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">n_obs</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">prop_y_train</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">prop_y_test</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">roc_auc_2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>statistics</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>F2_Logistic</th>\n",
+       "      <td>0.822960</td>\n",
+       "      <td>0.084018</td>\n",
+       "      <td>0.710190</td>\n",
+       "      <td>0.065621</td>\n",
+       "      <td>0.758944</td>\n",
+       "      <td>0.053574</td>\n",
+       "      <td>0.760531</td>\n",
+       "      <td>0.048169</td>\n",
+       "      <td>0.816217</td>\n",
+       "      <td>0.041837</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>360.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.555556</td>\n",
+       "      <td>0.013941</td>\n",
+       "      <td>0.555556</td>\n",
+       "      <td>0.055763</td>\n",
+       "      <td>0.816217</td>\n",
+       "      <td>0.041837</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_f2_te</th>\n",
+       "      <td>0.760457</td>\n",
+       "      <td>0.067588</td>\n",
+       "      <td>0.763575</td>\n",
+       "      <td>0.053465</td>\n",
+       "      <td>0.760331</td>\n",
+       "      <td>0.049311</td>\n",
+       "      <td>0.732516</td>\n",
+       "      <td>0.043720</td>\n",
+       "      <td>0.732516</td>\n",
+       "      <td>0.043720</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>360.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.555556</td>\n",
+       "      <td>0.013941</td>\n",
+       "      <td>0.555556</td>\n",
+       "      <td>0.055763</td>\n",
+       "      <td>0.732516</td>\n",
+       "      <td>0.043720</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "variable    precision              recall                  f1            \\\n",
+       "statistics       mean       std      mean       std      mean       std   \n",
+       "F2_Logistic  0.822960  0.084018  0.710190  0.065621  0.758944  0.053574   \n",
+       "F2_f2_te     0.760457  0.067588  0.763575  0.053465  0.760331  0.049311   \n",
+       "\n",
+       "variable    balanced_accuracy             roc_auc           num_feat       \\\n",
+       "statistics               mean       std      mean       std     mean  std   \n",
+       "F2_Logistic          0.760531  0.048169  0.816217  0.041837      1.0  0.0   \n",
+       "F2_f2_te             0.732516  0.043720  0.732516  0.043720      1.0  0.0   \n",
+       "\n",
+       "variable     n_obs      prop_y_train           prop_y_test            \\\n",
+       "statistics    mean  std         mean       std        mean       std   \n",
+       "F2_Logistic  360.0  0.0     0.555556  0.013941    0.555556  0.055763   \n",
+       "F2_f2_te     360.0  0.0     0.555556  0.013941    0.555556  0.055763   \n",
+       "\n",
+       "variable    roc_auc_2            \n",
+       "statistics       mean       std  \n",
+       "F2_Logistic  0.816217  0.041837  \n",
+       "F2_f2_te     0.732516  0.043720  "
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "_get_cv_means(results_dict)"
    ]
@@ -1904,9 +5916,316 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 134,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>run_00</th>\n",
+       "      <th>run_01</th>\n",
+       "      <th>run_02</th>\n",
+       "      <th>run_03</th>\n",
+       "      <th>run_04</th>\n",
+       "      <th>run_05</th>\n",
+       "      <th>run_06</th>\n",
+       "      <th>run_07</th>\n",
+       "      <th>run_08</th>\n",
+       "      <th>run_09</th>\n",
+       "      <th>...</th>\n",
+       "      <th>run_40</th>\n",
+       "      <th>run_41</th>\n",
+       "      <th>run_42</th>\n",
+       "      <th>run_43</th>\n",
+       "      <th>run_44</th>\n",
+       "      <th>run_45</th>\n",
+       "      <th>run_46</th>\n",
+       "      <th>run_47</th>\n",
+       "      <th>run_48</th>\n",
+       "      <th>run_49</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sample ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
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+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Plate6_G11</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.519821</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.543201</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.537871</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.547867</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plate1_F7</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.173315</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.165530</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.15088</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.182321</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plate6_D2</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.502471</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.473852</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.526301</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.529651</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plate6_C5</th>\n",
+       "      <td>0.877745</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.886994</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.845313</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.830246</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plate4_F8</th>\n",
+       "      <td>0.999994</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.999994</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.999986</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.999957</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 50 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              run_00  run_01  run_02    run_03    run_04  run_05  run_06  \\\n",
+       "Sample ID                                                                  \n",
+       "Plate6_G11       NaN     NaN     NaN       NaN  0.519821     NaN     NaN   \n",
+       "Plate1_F7        NaN     NaN     NaN  0.173315       NaN     NaN     NaN   \n",
+       "Plate6_D2        NaN     NaN     NaN  0.502471       NaN     NaN     NaN   \n",
+       "Plate6_C5   0.877745     NaN     NaN       NaN       NaN     NaN     NaN   \n",
+       "Plate4_F8   0.999994     NaN     NaN       NaN       NaN     NaN     NaN   \n",
+       "\n",
+       "              run_07    run_08    run_09  ...    run_40    run_41    run_42  \\\n",
+       "Sample ID                                 ...                                 \n",
+       "Plate6_G11       NaN  0.543201       NaN  ...  0.537871       NaN       NaN   \n",
+       "Plate1_F7        NaN  0.165530       NaN  ...       NaN       NaN       NaN   \n",
+       "Plate6_D2   0.473852       NaN       NaN  ...       NaN  0.526301       NaN   \n",
+       "Plate6_C5        NaN  0.886994       NaN  ...       NaN       NaN  0.845313   \n",
+       "Plate4_F8        NaN       NaN  0.999994  ...       NaN       NaN       NaN   \n",
+       "\n",
+       "             run_43    run_44    run_45  run_46    run_47  run_48    run_49  \n",
+       "Sample ID                                                                    \n",
+       "Plate6_G11      NaN       NaN  0.547867     NaN       NaN     NaN       NaN  \n",
+       "Plate1_F7   0.15088       NaN  0.182321     NaN       NaN     NaN       NaN  \n",
+       "Plate6_D2       NaN       NaN       NaN     NaN       NaN     NaN  0.529651  \n",
+       "Plate6_C5       NaN       NaN       NaN     NaN  0.830246     NaN       NaN  \n",
+       "Plate4_F8       NaN  0.999986       NaN     NaN  0.999957     NaN       NaN  \n",
+       "\n",
+       "[5 rows x 50 columns]"
+      ]
+     },
+     "metadata": {},
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+    },
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>n_pred</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sample ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Plate6_G11</th>\n",
+       "      <td>0.537701</td>\n",
+       "      <td>0.013726</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plate1_F7</th>\n",
+       "      <td>0.174547</td>\n",
+       "      <td>0.012473</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plate6_D2</th>\n",
+       "      <td>0.508038</td>\n",
+       "      <td>0.016623</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plate6_C5</th>\n",
+       "      <td>0.858803</td>\n",
+       "      <td>0.018440</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plate4_F8</th>\n",
+       "      <td>0.999986</td>\n",
+       "      <td>0.000014</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                mean       std  n_pred\n",
+       "Sample ID                             \n",
+       "Plate6_G11  0.537701  0.013726      10\n",
+       "Plate1_F7   0.174547  0.012473      10\n",
+       "Plate6_D2   0.508038  0.016623      10\n",
+       "Plate6_C5   0.858803  0.018440      10\n",
+       "Plate4_F8   0.999986  0.000014      10"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "_df = pd.DataFrame(index=_y.index)\n",
     "for _i, _y_pred in enumerate(results_dict['F2_Logistic']['y_test']):\n",
@@ -1920,7 +6239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1936,9 +6255,143 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 136,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>variable</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">precision</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">recall</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">f1</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">balanced_accuracy</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">roc_auc</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">num_feat</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">n_obs</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">prop_y_train</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">prop_y_test</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">roc_auc_2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>statistics</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>F2_Logistic</th>\n",
+       "      <td>0.822960</td>\n",
+       "      <td>0.084018</td>\n",
+       "      <td>0.710190</td>\n",
+       "      <td>0.065621</td>\n",
+       "      <td>0.758944</td>\n",
+       "      <td>0.053574</td>\n",
+       "      <td>0.760531</td>\n",
+       "      <td>0.048169</td>\n",
+       "      <td>0.816217</td>\n",
+       "      <td>0.041837</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>360.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.555556</td>\n",
+       "      <td>0.013941</td>\n",
+       "      <td>0.555556</td>\n",
+       "      <td>0.055763</td>\n",
+       "      <td>0.816217</td>\n",
+       "      <td>0.041837</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_f2_te</th>\n",
+       "      <td>0.760457</td>\n",
+       "      <td>0.067588</td>\n",
+       "      <td>0.763575</td>\n",
+       "      <td>0.053465</td>\n",
+       "      <td>0.760331</td>\n",
+       "      <td>0.049311</td>\n",
+       "      <td>0.732516</td>\n",
+       "      <td>0.043720</td>\n",
+       "      <td>0.732516</td>\n",
+       "      <td>0.043720</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>360.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.555556</td>\n",
+       "      <td>0.013941</td>\n",
+       "      <td>0.555556</td>\n",
+       "      <td>0.055763</td>\n",
+       "      <td>0.732516</td>\n",
+       "      <td>0.043720</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "variable    precision              recall                  f1            \\\n",
+       "statistics       mean       std      mean       std      mean       std   \n",
+       "F2_Logistic  0.822960  0.084018  0.710190  0.065621  0.758944  0.053574   \n",
+       "F2_f2_te     0.760457  0.067588  0.763575  0.053465  0.760331  0.049311   \n",
+       "\n",
+       "variable    balanced_accuracy             roc_auc           num_feat       \\\n",
+       "statistics               mean       std      mean       std     mean  std   \n",
+       "F2_Logistic          0.760531  0.048169  0.816217  0.041837      1.0  0.0   \n",
+       "F2_f2_te             0.732516  0.043720  0.732516  0.043720      1.0  0.0   \n",
+       "\n",
+       "variable     n_obs      prop_y_train           prop_y_test            \\\n",
+       "statistics    mean  std         mean       std        mean       std   \n",
+       "F2_Logistic  360.0  0.0     0.555556  0.013941    0.555556  0.055763   \n",
+       "F2_f2_te     360.0  0.0     0.555556  0.013941    0.555556  0.055763   \n",
+       "\n",
+       "variable    roc_auc_2            \n",
+       "statistics       mean       std  \n",
+       "F2_Logistic  0.816217  0.041837  \n",
+       "F2_f2_te     0.732516  0.043720  "
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "result_metrics = _get_cv_means(results_dict)\n",
     "result_metrics"
@@ -1946,9 +6399,143 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 137,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>variable</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">balanced_accuracy</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">f1</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">n_obs</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">num_feat</th>\n",
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+       "      <th colspan=\"2\" halign=\"left\">prop_y_test</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">prop_y_train</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">recall</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">roc_auc</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">roc_auc_2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
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+       "      <th>lower</th>\n",
+       "      <th>upper</th>\n",
+       "      <th>lower</th>\n",
+       "      <th>upper</th>\n",
+       "      <th>lower</th>\n",
+       "      <th>upper</th>\n",
+       "      <th>lower</th>\n",
+       "      <th>upper</th>\n",
+       "      <th>lower</th>\n",
+       "      <th>upper</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>F2_Logistic</th>\n",
+       "      <td>0.664193</td>\n",
+       "      <td>0.856868</td>\n",
+       "      <td>0.651796</td>\n",
+       "      <td>0.866093</td>\n",
+       "      <td>360.0</td>\n",
+       "      <td>360.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.654924</td>\n",
+       "      <td>0.990995</td>\n",
+       "      <td>0.444029</td>\n",
+       "      <td>0.667083</td>\n",
+       "      <td>0.527674</td>\n",
+       "      <td>0.583437</td>\n",
+       "      <td>0.578948</td>\n",
+       "      <td>0.841432</td>\n",
+       "      <td>0.732543</td>\n",
+       "      <td>0.899891</td>\n",
+       "      <td>0.732543</td>\n",
+       "      <td>0.899891</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_f2_te</th>\n",
+       "      <td>0.645075</td>\n",
+       "      <td>0.819957</td>\n",
+       "      <td>0.661709</td>\n",
+       "      <td>0.858954</td>\n",
+       "      <td>360.0</td>\n",
+       "      <td>360.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.625281</td>\n",
+       "      <td>0.895633</td>\n",
+       "      <td>0.444029</td>\n",
+       "      <td>0.667083</td>\n",
+       "      <td>0.527674</td>\n",
+       "      <td>0.583437</td>\n",
+       "      <td>0.656645</td>\n",
+       "      <td>0.870506</td>\n",
+       "      <td>0.645075</td>\n",
+       "      <td>0.819957</td>\n",
+       "      <td>0.645075</td>\n",
+       "      <td>0.819957</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "variable    balanced_accuracy                  f1            n_obs         \\\n",
+       "                        lower     upper     lower     upper  lower  upper   \n",
+       "F2_Logistic          0.664193  0.856868  0.651796  0.866093  360.0  360.0   \n",
+       "F2_f2_te             0.645075  0.819957  0.661709  0.858954  360.0  360.0   \n",
+       "\n",
+       "variable    num_feat       precision           prop_y_test            \\\n",
+       "               lower upper     lower     upper       lower     upper   \n",
+       "F2_Logistic      1.0   1.0  0.654924  0.990995    0.444029  0.667083   \n",
+       "F2_f2_te         1.0   1.0  0.625281  0.895633    0.444029  0.667083   \n",
+       "\n",
+       "variable    prop_y_train              recall             roc_auc            \\\n",
+       "                   lower     upper     lower     upper     lower     upper   \n",
+       "F2_Logistic     0.527674  0.583437  0.578948  0.841432  0.732543  0.899891   \n",
+       "F2_f2_te        0.527674  0.583437  0.656645  0.870506  0.645075  0.819957   \n",
+       "\n",
+       "variable    roc_auc_2            \n",
+       "                lower     upper  \n",
+       "F2_Logistic  0.732543  0.899891  \n",
+       "F2_f2_te     0.645075  0.819957  "
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "def create_95CI_df(df_metrics, selected_metrics=None):\n",
     "    \"\"\"Expects output from _get_cv_means.\"\"\"\n",
@@ -1990,7 +6577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 138,
    "metadata": {
     "Collapsed": "false"
    },
@@ -2022,9 +6609,114 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 139,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>F2 (k=10)</th>\n",
+       "      <th>F2 (k=5)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Protein ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>P10643</th>\n",
+       "      <td>C7</td>\n",
+       "      <td>C7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P19320</th>\n",
+       "      <td>VCAM1</td>\n",
+       "      <td>VCAM1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q16270</th>\n",
+       "      <td>IGFBP7</td>\n",
+       "      <td>IGFBP7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P35858</th>\n",
+       "      <td>IGFALS</td>\n",
+       "      <td>IGFALS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P02743</th>\n",
+       "      <td>APCS</td>\n",
+       "      <td>APCS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A0A0G2JMB2</th>\n",
+       "      <td>IGHA2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>O00391</th>\n",
+       "      <td>QSOX1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q08380</th>\n",
+       "      <td>LGALS3BP</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P01833</th>\n",
+       "      <td>PIGR</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P00739</th>\n",
+       "      <td>HPR</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           F2 (k=10) F2 (k=5)\n",
+       "Protein ID                   \n",
+       "P10643            C7       C7\n",
+       "P19320         VCAM1    VCAM1\n",
+       "Q16270        IGFBP7   IGFBP7\n",
+       "P35858        IGFALS   IGFALS\n",
+       "P02743          APCS     APCS\n",
+       "A0A0G2JMB2     IGHA2      NaN\n",
+       "O00391         QSOX1      NaN\n",
+       "Q08380      LGALS3BP      NaN\n",
+       "P01833          PIGR      NaN\n",
+       "P00739           HPR      NaN"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from src.sklearn import FeatureSelector\n",
     "    \n",
@@ -2053,7 +6745,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2118,9 +6810,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 226,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "summary_n_features = pd.read_pickle('data/processed/summary_n_features_msproteomics_bioRxiv.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(5,5))\n",
     "sns.lineplot(x='n_features',y='roc_auc',hue='test_case', data=summary_n_features)\n",
@@ -2132,18 +6846,260 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 228,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1</th>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <th>roc_auc</th>\n",
+       "      <th>num_feat</th>\n",
+       "      <th>n_obs</th>\n",
+       "      <th>roc_auc_2</th>\n",
+       "      <th>name</th>\n",
+       "      <th>n_features</th>\n",
+       "      <th>test_case</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.582</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.736</td>\n",
+       "      <td>0.562</td>\n",
+       "      <td>0.930</td>\n",
+       "      <td>1</td>\n",
+       "      <td>358</td>\n",
+       "      <td>0.930</td>\n",
+       "      <td>LogisticRegression</td>\n",
+       "      <td>1</td>\n",
+       "      <td>F2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.603</td>\n",
+       "      <td>0.976</td>\n",
+       "      <td>0.745</td>\n",
+       "      <td>0.538</td>\n",
+       "      <td>0.869</td>\n",
+       "      <td>1</td>\n",
+       "      <td>358</td>\n",
+       "      <td>0.869</td>\n",
+       "      <td>LogisticRegression</td>\n",
+       "      <td>1</td>\n",
+       "      <td>F2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.529</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.692</td>\n",
+       "      <td>0.529</td>\n",
+       "      <td>0.802</td>\n",
+       "      <td>1</td>\n",
+       "      <td>358</td>\n",
+       "      <td>0.802</td>\n",
+       "      <td>LogisticRegression</td>\n",
+       "      <td>1</td>\n",
+       "      <td>F2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.557</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.716</td>\n",
+       "      <td>0.516</td>\n",
+       "      <td>0.925</td>\n",
+       "      <td>1</td>\n",
+       "      <td>358</td>\n",
+       "      <td>0.925</td>\n",
+       "      <td>LogisticRegression</td>\n",
+       "      <td>1</td>\n",
+       "      <td>F2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.606</td>\n",
+       "      <td>0.952</td>\n",
+       "      <td>0.741</td>\n",
+       "      <td>0.543</td>\n",
+       "      <td>0.821</td>\n",
+       "      <td>1</td>\n",
+       "      <td>358</td>\n",
+       "      <td>0.821</td>\n",
+       "      <td>LogisticRegression</td>\n",
+       "      <td>1</td>\n",
+       "      <td>F2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>0.917</td>\n",
+       "      <td>0.647</td>\n",
+       "      <td>0.759</td>\n",
+       "      <td>0.796</td>\n",
+       "      <td>0.854</td>\n",
+       "      <td>49</td>\n",
+       "      <td>350</td>\n",
+       "      <td>0.854</td>\n",
+       "      <td>LogisticRegression</td>\n",
+       "      <td>49</td>\n",
+       "      <td>I2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>0.750</td>\n",
+       "      <td>0.805</td>\n",
+       "      <td>0.776</td>\n",
+       "      <td>0.713</td>\n",
+       "      <td>0.801</td>\n",
+       "      <td>49</td>\n",
+       "      <td>350</td>\n",
+       "      <td>0.801</td>\n",
+       "      <td>LogisticRegression</td>\n",
+       "      <td>49</td>\n",
+       "      <td>I2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>0.667</td>\n",
+       "      <td>0.667</td>\n",
+       "      <td>0.667</td>\n",
+       "      <td>0.685</td>\n",
+       "      <td>0.749</td>\n",
+       "      <td>49</td>\n",
+       "      <td>350</td>\n",
+       "      <td>0.749</td>\n",
+       "      <td>LogisticRegression</td>\n",
+       "      <td>49</td>\n",
+       "      <td>I2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>0.818</td>\n",
+       "      <td>0.692</td>\n",
+       "      <td>0.750</td>\n",
+       "      <td>0.746</td>\n",
+       "      <td>0.850</td>\n",
+       "      <td>49</td>\n",
+       "      <td>350</td>\n",
+       "      <td>0.850</td>\n",
+       "      <td>LogisticRegression</td>\n",
+       "      <td>49</td>\n",
+       "      <td>I2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>0.729</td>\n",
+       "      <td>0.854</td>\n",
+       "      <td>0.787</td>\n",
+       "      <td>0.710</td>\n",
+       "      <td>0.797</td>\n",
+       "      <td>49</td>\n",
+       "      <td>350</td>\n",
+       "      <td>0.797</td>\n",
+       "      <td>LogisticRegression</td>\n",
+       "      <td>49</td>\n",
+       "      <td>I2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>9800 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    precision  recall     f1  balanced_accuracy  roc_auc  num_feat  n_obs  \\\n",
+       "0       0.582   1.000  0.736              0.562    0.930         1    358   \n",
+       "1       0.603   0.976  0.745              0.538    0.869         1    358   \n",
+       "2       0.529   1.000  0.692              0.529    0.802         1    358   \n",
+       "3       0.557   1.000  0.716              0.516    0.925         1    358   \n",
+       "4       0.606   0.952  0.741              0.543    0.821         1    358   \n",
+       "..        ...     ...    ...                ...      ...       ...    ...   \n",
+       "45      0.917   0.647  0.759              0.796    0.854        49    350   \n",
+       "46      0.750   0.805  0.776              0.713    0.801        49    350   \n",
+       "47      0.667   0.667  0.667              0.685    0.749        49    350   \n",
+       "48      0.818   0.692  0.750              0.746    0.850        49    350   \n",
+       "49      0.729   0.854  0.787              0.710    0.797        49    350   \n",
+       "\n",
+       "    roc_auc_2                name  n_features test_case  \n",
+       "0       0.930  LogisticRegression           1        F2  \n",
+       "1       0.869  LogisticRegression           1        F2  \n",
+       "2       0.802  LogisticRegression           1        F2  \n",
+       "3       0.925  LogisticRegression           1        F2  \n",
+       "4       0.821  LogisticRegression           1        F2  \n",
+       "..        ...                 ...         ...       ...  \n",
+       "45      0.854  LogisticRegression          49        I2  \n",
+       "46      0.801  LogisticRegression          49        I2  \n",
+       "47      0.749  LogisticRegression          49        I2  \n",
+       "48      0.850  LogisticRegression          49        I2  \n",
+       "49      0.797  LogisticRegression          49        I2  \n",
+       "\n",
+       "[9800 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 228,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "summary_n_features"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 229,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1044x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig=plt.figure(figsize=(14.5, 4))\n",
     "metrics = ['f1', 'balanced_accuracy', 'roc_auc']\n",
@@ -2161,9 +7117,127 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 230,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1</th>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <th>roc_auc</th>\n",
+       "      <th>num_feat</th>\n",
+       "      <th>n_obs</th>\n",
+       "      <th>roc_auc_2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test_case</th>\n",
+       "      <th>n_features</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>S1</th>\n",
+       "      <th>28</th>\n",
+       "      <td>0.821</td>\n",
+       "      <td>0.838</td>\n",
+       "      <td>0.827</td>\n",
+       "      <td>0.804</td>\n",
+       "      <td>0.889</td>\n",
+       "      <td>28</td>\n",
+       "      <td>350</td>\n",
+       "      <td>0.889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3</th>\n",
+       "      <th>21</th>\n",
+       "      <td>0.878</td>\n",
+       "      <td>0.780</td>\n",
+       "      <td>0.823</td>\n",
+       "      <td>0.871</td>\n",
+       "      <td>0.962</td>\n",
+       "      <td>21</td>\n",
+       "      <td>358</td>\n",
+       "      <td>0.962</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2</th>\n",
+       "      <th>14</th>\n",
+       "      <td>0.847</td>\n",
+       "      <td>0.801</td>\n",
+       "      <td>0.821</td>\n",
+       "      <td>0.808</td>\n",
+       "      <td>0.881</td>\n",
+       "      <td>14</td>\n",
+       "      <td>358</td>\n",
+       "      <td>0.881</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I2</th>\n",
+       "      <th>9</th>\n",
+       "      <td>0.780</td>\n",
+       "      <td>0.771</td>\n",
+       "      <td>0.773</td>\n",
+       "      <td>0.760</td>\n",
+       "      <td>0.831</td>\n",
+       "      <td>9</td>\n",
+       "      <td>350</td>\n",
+       "      <td>0.831</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      precision  recall     f1  balanced_accuracy  roc_auc  \\\n",
+       "test_case n_features                                                         \n",
+       "S1        28              0.821   0.838  0.827              0.804    0.889   \n",
+       "F3        21              0.878   0.780  0.823              0.871    0.962   \n",
+       "F2        14              0.847   0.801  0.821              0.808    0.881   \n",
+       "I2        9               0.780   0.771  0.773              0.760    0.831   \n",
+       "\n",
+       "                      num_feat  n_obs  roc_auc_2  \n",
+       "test_case n_features                              \n",
+       "S1        28                28    350      0.889  \n",
+       "F3        21                21    358      0.962  \n",
+       "F2        14                14    358      0.881  \n",
+       "I2        9                  9    350      0.831  "
+      ]
+     },
+     "execution_count": 230,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "combined = summary_n_features.groupby(['test_case','n_features']).mean()\n",
     "\n",
@@ -2173,18 +7247,347 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 231,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
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+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1</th>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <th>roc_auc</th>\n",
+       "      <th>num_feat</th>\n",
+       "      <th>n_obs</th>\n",
+       "      <th>roc_auc_2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test_case</th>\n",
+       "      <th>n_features</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">F2</th>\n",
+       "      <th>1</th>\n",
+       "      <td>0.579</td>\n",
+       "      <td>0.984</td>\n",
+       "      <td>0.727</td>\n",
+       "      <td>0.545</td>\n",
+       "      <td>0.868</td>\n",
+       "      <td>1</td>\n",
+       "      <td>358</td>\n",
+       "      <td>0.868</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.578</td>\n",
+       "      <td>0.980</td>\n",
+       "      <td>0.725</td>\n",
+       "      <td>0.543</td>\n",
+       "      <td>0.849</td>\n",
+       "      <td>2</td>\n",
+       "      <td>358</td>\n",
+       "      <td>0.849</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.597</td>\n",
+       "      <td>0.962</td>\n",
+       "      <td>0.734</td>\n",
+       "      <td>0.576</td>\n",
+       "      <td>0.821</td>\n",
+       "      <td>3</td>\n",
+       "      <td>358</td>\n",
+       "      <td>0.821</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.827</td>\n",
+       "      <td>0.779</td>\n",
+       "      <td>0.800</td>\n",
+       "      <td>0.787</td>\n",
+       "      <td>0.882</td>\n",
+       "      <td>4</td>\n",
+       "      <td>358</td>\n",
+       "      <td>0.882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.834</td>\n",
+       "      <td>0.779</td>\n",
+       "      <td>0.803</td>\n",
+       "      <td>0.792</td>\n",
+       "      <td>0.883</td>\n",
+       "      <td>5</td>\n",
+       "      <td>358</td>\n",
+       "      <td>0.883</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">S1</th>\n",
+       "      <th>45</th>\n",
+       "      <td>0.817</td>\n",
+       "      <td>0.821</td>\n",
+       "      <td>0.817</td>\n",
+       "      <td>0.795</td>\n",
+       "      <td>0.876</td>\n",
+       "      <td>45</td>\n",
+       "      <td>350</td>\n",
+       "      <td>0.876</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>0.819</td>\n",
+       "      <td>0.820</td>\n",
+       "      <td>0.817</td>\n",
+       "      <td>0.797</td>\n",
+       "      <td>0.874</td>\n",
+       "      <td>46</td>\n",
+       "      <td>350</td>\n",
+       "      <td>0.874</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>0.820</td>\n",
+       "      <td>0.819</td>\n",
+       "      <td>0.817</td>\n",
+       "      <td>0.797</td>\n",
+       "      <td>0.872</td>\n",
+       "      <td>47</td>\n",
+       "      <td>350</td>\n",
+       "      <td>0.872</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>0.821</td>\n",
+       "      <td>0.819</td>\n",
+       "      <td>0.817</td>\n",
+       "      <td>0.797</td>\n",
+       "      <td>0.872</td>\n",
+       "      <td>48</td>\n",
+       "      <td>350</td>\n",
+       "      <td>0.872</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>0.822</td>\n",
+       "      <td>0.819</td>\n",
+       "      <td>0.818</td>\n",
+       "      <td>0.798</td>\n",
+       "      <td>0.871</td>\n",
+       "      <td>49</td>\n",
+       "      <td>350</td>\n",
+       "      <td>0.871</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>196 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      precision  recall     f1  balanced_accuracy  roc_auc  \\\n",
+       "test_case n_features                                                         \n",
+       "F2        1               0.579   0.984  0.727              0.545    0.868   \n",
+       "          2               0.578   0.980  0.725              0.543    0.849   \n",
+       "          3               0.597   0.962  0.734              0.576    0.821   \n",
+       "          4               0.827   0.779  0.800              0.787    0.882   \n",
+       "          5               0.834   0.779  0.803              0.792    0.883   \n",
+       "...                         ...     ...    ...                ...      ...   \n",
+       "S1        45              0.817   0.821  0.817              0.795    0.876   \n",
+       "          46              0.819   0.820  0.817              0.797    0.874   \n",
+       "          47              0.820   0.819  0.817              0.797    0.872   \n",
+       "          48              0.821   0.819  0.817              0.797    0.872   \n",
+       "          49              0.822   0.819  0.818              0.798    0.871   \n",
+       "\n",
+       "                      num_feat  n_obs  roc_auc_2  \n",
+       "test_case n_features                              \n",
+       "F2        1                  1    358      0.868  \n",
+       "          2                  2    358      0.849  \n",
+       "          3                  3    358      0.821  \n",
+       "          4                  4    358      0.882  \n",
+       "          5                  5    358      0.883  \n",
+       "...                        ...    ...        ...  \n",
+       "S1        45                45    350      0.876  \n",
+       "          46                46    350      0.874  \n",
+       "          47                47    350      0.872  \n",
+       "          48                48    350      0.872  \n",
+       "          49                49    350      0.871  \n",
+       "\n",
+       "[196 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 231,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "combined"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 232,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1</th>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <th>roc_auc</th>\n",
+       "      <th>num_feat</th>\n",
+       "      <th>n_obs</th>\n",
+       "      <th>roc_auc_2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test_case</th>\n",
+       "      <th>n_features</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>F2</th>\n",
+       "      <th>1</th>\n",
+       "      <td>0.579</td>\n",
+       "      <td>0.984</td>\n",
+       "      <td>0.727</td>\n",
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+       "      <td>358</td>\n",
+       "      <td>0.868</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3</th>\n",
+       "      <th>1</th>\n",
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+       "      <td>0.000</td>\n",
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+       "      <td>358</td>\n",
+       "      <td>0.930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I2</th>\n",
+       "      <th>1</th>\n",
+       "      <td>0.598</td>\n",
+       "      <td>0.973</td>\n",
+       "      <td>0.736</td>\n",
+       "      <td>0.605</td>\n",
+       "      <td>0.810</td>\n",
+       "      <td>1</td>\n",
+       "      <td>350</td>\n",
+       "      <td>0.810</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>S1</th>\n",
+       "      <th>1</th>\n",
+       "      <td>0.673</td>\n",
+       "      <td>0.875</td>\n",
+       "      <td>0.758</td>\n",
+       "      <td>0.669</td>\n",
+       "      <td>0.818</td>\n",
+       "      <td>1</td>\n",
+       "      <td>350</td>\n",
+       "      <td>0.818</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      precision  recall     f1  balanced_accuracy  roc_auc  \\\n",
+       "test_case n_features                                                         \n",
+       "F2        1               0.579   0.984  0.727              0.545    0.868   \n",
+       "F3        1               0.000   0.000  0.000              0.500    0.930   \n",
+       "I2        1               0.598   0.973  0.736              0.605    0.810   \n",
+       "S1        1               0.673   0.875  0.758              0.669    0.818   \n",
+       "\n",
+       "                      num_feat  n_obs  roc_auc_2  \n",
+       "test_case n_features                              \n",
+       "F2        1                  1    358      0.868  \n",
+       "F3        1                  1    358      0.930  \n",
+       "I2        1                  1    350      0.810  \n",
+       "S1        1                  1    350      0.818  "
+      ]
+     },
+     "execution_count": 232,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "single_protein = combined[combined['num_feat']==1]\n",
     "single_protein"
@@ -2192,9 +7595,179 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 233,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1</th>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <th>roc_auc</th>\n",
+       "      <th>num_feat</th>\n",
+       "      <th>n_obs</th>\n",
+       "      <th>roc_auc_2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test_case</th>\n",
+       "      <th>n_features</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">F2</th>\n",
+       "      <th>14</th>\n",
+       "      <td>0.847</td>\n",
+       "      <td>0.801</td>\n",
+       "      <td>0.821</td>\n",
+       "      <td>0.808</td>\n",
+       "      <td>0.881</td>\n",
+       "      <td>14</td>\n",
+       "      <td>358</td>\n",
+       "      <td>0.881</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.579</td>\n",
+       "      <td>0.984</td>\n",
+       "      <td>0.727</td>\n",
+       "      <td>0.545</td>\n",
+       "      <td>0.868</td>\n",
+       "      <td>1</td>\n",
+       "      <td>358</td>\n",
+       "      <td>0.868</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">F3</th>\n",
+       "      <th>21</th>\n",
+       "      <td>0.878</td>\n",
+       "      <td>0.780</td>\n",
+       "      <td>0.823</td>\n",
+       "      <td>0.871</td>\n",
+       "      <td>0.962</td>\n",
+       "      <td>21</td>\n",
+       "      <td>358</td>\n",
+       "      <td>0.962</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.500</td>\n",
+       "      <td>0.930</td>\n",
+       "      <td>1</td>\n",
+       "      <td>358</td>\n",
+       "      <td>0.930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">I2</th>\n",
+       "      <th>9</th>\n",
+       "      <td>0.780</td>\n",
+       "      <td>0.771</td>\n",
+       "      <td>0.773</td>\n",
+       "      <td>0.760</td>\n",
+       "      <td>0.831</td>\n",
+       "      <td>9</td>\n",
+       "      <td>350</td>\n",
+       "      <td>0.831</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.598</td>\n",
+       "      <td>0.973</td>\n",
+       "      <td>0.736</td>\n",
+       "      <td>0.605</td>\n",
+       "      <td>0.810</td>\n",
+       "      <td>1</td>\n",
+       "      <td>350</td>\n",
+       "      <td>0.810</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">S1</th>\n",
+       "      <th>28</th>\n",
+       "      <td>0.821</td>\n",
+       "      <td>0.838</td>\n",
+       "      <td>0.827</td>\n",
+       "      <td>0.804</td>\n",
+       "      <td>0.889</td>\n",
+       "      <td>28</td>\n",
+       "      <td>350</td>\n",
+       "      <td>0.889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.673</td>\n",
+       "      <td>0.875</td>\n",
+       "      <td>0.758</td>\n",
+       "      <td>0.669</td>\n",
+       "      <td>0.818</td>\n",
+       "      <td>1</td>\n",
+       "      <td>350</td>\n",
+       "      <td>0.818</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      precision  recall     f1  balanced_accuracy  roc_auc  \\\n",
+       "test_case n_features                                                         \n",
+       "F2        14              0.847   0.801  0.821              0.808    0.881   \n",
+       "          1               0.579   0.984  0.727              0.545    0.868   \n",
+       "F3        21              0.878   0.780  0.823              0.871    0.962   \n",
+       "          1               0.000   0.000  0.000              0.500    0.930   \n",
+       "I2        9               0.780   0.771  0.773              0.760    0.831   \n",
+       "          1               0.598   0.973  0.736              0.605    0.810   \n",
+       "S1        28              0.821   0.838  0.827              0.804    0.889   \n",
+       "          1               0.673   0.875  0.758              0.669    0.818   \n",
+       "\n",
+       "                      num_feat  n_obs  roc_auc_2  \n",
+       "test_case n_features                              \n",
+       "F2        14                14    358      0.881  \n",
+       "          1                  1    358      0.868  \n",
+       "F3        21                21    358      0.962  \n",
+       "          1                  1    358      0.930  \n",
+       "I2        9                  9    350      0.831  \n",
+       "          1                  1    350      0.810  \n",
+       "S1        28                28    350      0.889  \n",
+       "          1                  1    350      0.818  "
+      ]
+     },
+     "execution_count": 233,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "one_vs_panel = pd.concat([best, single_protein]).sort_values(by='test_case')\n",
     "one_vs_panel.to_csv('tables/1_vs_panel.csv')\n",
@@ -2203,9 +7776,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 234,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'S1': 28, 'F3': 21, 'F2': 14, 'I2': 9}"
+      ]
+     },
+     "execution_count": 234,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "best_dict = {}\n",
     "for i, j in best.index.to_list():\n",
@@ -2227,7 +7811,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 235,
    "metadata": {
     "Collapsed": "false"
    },
@@ -2249,43 +7833,361 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 236,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Protein ID</th>\n",
+       "      <th>P10643</th>\n",
+       "      <th>P19320</th>\n",
+       "      <th>Q16270</th>\n",
+       "      <th>P35858</th>\n",
+       "      <th>P02743</th>\n",
+       "      <th>A0A0G2JMB2</th>\n",
+       "      <th>O00391</th>\n",
+       "      <th>Q08380</th>\n",
+       "      <th>P01833</th>\n",
+       "      <th>P00739</th>\n",
+       "      <th>A0A0A0MRZ8</th>\n",
+       "      <th>Q99650</th>\n",
+       "      <th>Q9Y5Y7</th>\n",
+       "      <th>Q15582</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>F2</th>\n",
+       "      <td>C7</td>\n",
+       "      <td>VCAM1</td>\n",
+       "      <td>IGFBP7</td>\n",
+       "      <td>IGFALS</td>\n",
+       "      <td>APCS</td>\n",
+       "      <td>IGHA2</td>\n",
+       "      <td>QSOX1</td>\n",
+       "      <td>LGALS3BP</td>\n",
+       "      <td>PIGR</td>\n",
+       "      <td>HPR</td>\n",
+       "      <td>IGKV3D-11</td>\n",
+       "      <td>OSMR</td>\n",
+       "      <td>LYVE1</td>\n",
+       "      <td>TGFBI</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Protein ID P10643 P19320  Q16270  P35858 P02743 A0A0G2JMB2 O00391    Q08380  \\\n",
+       "F2             C7  VCAM1  IGFBP7  IGFALS   APCS      IGHA2  QSOX1  LGALS3BP   \n",
+       "\n",
+       "Protein ID P01833 P00739 A0A0A0MRZ8 Q99650 Q9Y5Y7 Q15582  \n",
+       "F2           PIGR    HPR  IGKV3D-11   OSMR  LYVE1  TGFBI  "
+      ]
+     },
+     "execution_count": 236,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "proteins_selected_f2.T"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 237,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Protein ID</th>\n",
+       "      <th>P10643</th>\n",
+       "      <th>P19320</th>\n",
+       "      <th>O00391</th>\n",
+       "      <th>P02743</th>\n",
+       "      <th>Q16270</th>\n",
+       "      <th>Q08380</th>\n",
+       "      <th>A0A286YEY1</th>\n",
+       "      <th>P27169</th>\n",
+       "      <th>Q9Y5Y7</th>\n",
+       "      <th>P51884</th>\n",
+       "      <th>...</th>\n",
+       "      <th>P05546</th>\n",
+       "      <th>A0A0G2JMB2</th>\n",
+       "      <th>Q99650</th>\n",
+       "      <th>P01834</th>\n",
+       "      <th>P35858</th>\n",
+       "      <th>P01619</th>\n",
+       "      <th>K7ERI9</th>\n",
+       "      <th>Q15485</th>\n",
+       "      <th>Q15582</th>\n",
+       "      <th>P0DOY2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>F3</th>\n",
+       "      <td>C7</td>\n",
+       "      <td>VCAM1</td>\n",
+       "      <td>QSOX1</td>\n",
+       "      <td>APCS</td>\n",
+       "      <td>IGFBP7</td>\n",
+       "      <td>LGALS3BP</td>\n",
+       "      <td>IGHA1</td>\n",
+       "      <td>PON1</td>\n",
+       "      <td>LYVE1</td>\n",
+       "      <td>LUM</td>\n",
+       "      <td>...</td>\n",
+       "      <td>SERPIND1</td>\n",
+       "      <td>IGHA2</td>\n",
+       "      <td>OSMR</td>\n",
+       "      <td>IGKC</td>\n",
+       "      <td>IGFALS</td>\n",
+       "      <td>IGKV3-20</td>\n",
+       "      <td>APOC1</td>\n",
+       "      <td>FCN2</td>\n",
+       "      <td>TGFBI</td>\n",
+       "      <td>IGLC2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Protein ID P10643 P19320 O00391 P02743  Q16270    Q08380 A0A286YEY1 P27169  \\\n",
+       "F3             C7  VCAM1  QSOX1   APCS  IGFBP7  LGALS3BP      IGHA1   PON1   \n",
+       "\n",
+       "Protein ID Q9Y5Y7 P51884  ...    P05546 A0A0G2JMB2 Q99650 P01834  P35858  \\\n",
+       "F3          LYVE1    LUM  ...  SERPIND1      IGHA2   OSMR   IGKC  IGFALS   \n",
+       "\n",
+       "Protein ID    P01619 K7ERI9 Q15485 Q15582 P0DOY2  \n",
+       "F3          IGKV3-20  APOC1   FCN2  TGFBI  IGLC2  \n",
+       "\n",
+       "[1 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 237,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "proteins_selected_f3.T"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 238,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Protein ID</th>\n",
+       "      <th>P05062</th>\n",
+       "      <th>Q08380</th>\n",
+       "      <th>H7BY64</th>\n",
+       "      <th>P06396</th>\n",
+       "      <th>C9JPQ9</th>\n",
+       "      <th>P19320</th>\n",
+       "      <th>Q15582</th>\n",
+       "      <th>P55103</th>\n",
+       "      <th>P08519</th>\n",
+       "      <th>Q92820</th>\n",
+       "      <th>...</th>\n",
+       "      <th>P43652</th>\n",
+       "      <th>P04196</th>\n",
+       "      <th>Q9Y5Y7</th>\n",
+       "      <th>B0YIW2</th>\n",
+       "      <th>P80748</th>\n",
+       "      <th>A0A0U1RR20</th>\n",
+       "      <th>P09172</th>\n",
+       "      <th>P01009</th>\n",
+       "      <th>P0C0L5</th>\n",
+       "      <th>A0A182DWH7</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>S1</th>\n",
+       "      <td>ALDOB</td>\n",
+       "      <td>LGALS3BP</td>\n",
+       "      <td>NoGene</td>\n",
+       "      <td>GSN</td>\n",
+       "      <td>FGG</td>\n",
+       "      <td>VCAM1</td>\n",
+       "      <td>TGFBI</td>\n",
+       "      <td>INHBC</td>\n",
+       "      <td>LPA</td>\n",
+       "      <td>GGH</td>\n",
+       "      <td>...</td>\n",
+       "      <td>AFM</td>\n",
+       "      <td>HRG</td>\n",
+       "      <td>LYVE1</td>\n",
+       "      <td>APOC3</td>\n",
+       "      <td>IGLV3-21</td>\n",
+       "      <td>PRG4</td>\n",
+       "      <td>DBH</td>\n",
+       "      <td>SERPINA1</td>\n",
+       "      <td>C4B</td>\n",
+       "      <td>SELENOP</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Protein ID P05062    Q08380  H7BY64 P06396 C9JPQ9 P19320 Q15582 P55103 P08519  \\\n",
+       "S1          ALDOB  LGALS3BP  NoGene    GSN    FGG  VCAM1  TGFBI  INHBC    LPA   \n",
+       "\n",
+       "Protein ID Q92820  ... P43652 P04196 Q9Y5Y7 B0YIW2    P80748 A0A0U1RR20  \\\n",
+       "S1            GGH  ...    AFM    HRG  LYVE1  APOC3  IGLV3-21       PRG4   \n",
+       "\n",
+       "Protein ID P09172    P01009 P0C0L5 A0A182DWH7  \n",
+       "S1            DBH  SERPINA1    C4B    SELENOP  \n",
+       "\n",
+       "[1 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 238,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "proteins_selected_s1.T"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 239,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Protein ID</th>\n",
+       "      <th>Q08380</th>\n",
+       "      <th>P10643</th>\n",
+       "      <th>P01833</th>\n",
+       "      <th>Q16270</th>\n",
+       "      <th>O00391</th>\n",
+       "      <th>P10909</th>\n",
+       "      <th>P23142</th>\n",
+       "      <th>P05362</th>\n",
+       "      <th>P05546</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>I2</th>\n",
+       "      <td>LGALS3BP</td>\n",
+       "      <td>C7</td>\n",
+       "      <td>PIGR</td>\n",
+       "      <td>IGFBP7</td>\n",
+       "      <td>QSOX1</td>\n",
+       "      <td>CLU</td>\n",
+       "      <td>FBLN1</td>\n",
+       "      <td>ICAM1</td>\n",
+       "      <td>SERPIND1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Protein ID    Q08380 P10643 P01833  Q16270 O00391 P10909 P23142 P05362  \\\n",
+       "I2          LGALS3BP     C7   PIGR  IGFBP7  QSOX1    CLU  FBLN1  ICAM1   \n",
+       "\n",
+       "Protein ID    P05546  \n",
+       "I2          SERPIND1  "
+      ]
+     },
+     "execution_count": 239,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "proteins_selected_I2.T"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 240,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2299,7 +8201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 241,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2324,9 +8226,137 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 242,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>F2</th>\n",
+       "      <th>I2</th>\n",
+       "      <th>S1</th>\n",
+       "      <th>Gene Name</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Protein ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>P10643</th>\n",
+       "      <td>0.263</td>\n",
+       "      <td>0.148</td>\n",
+       "      <td>-</td>\n",
+       "      <td>C7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P19320</th>\n",
+       "      <td>0.201</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.085</td>\n",
+       "      <td>VCAM1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q16270</th>\n",
+       "      <td>0.193</td>\n",
+       "      <td>0.121</td>\n",
+       "      <td>-</td>\n",
+       "      <td>IGFBP7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P35858</th>\n",
+       "      <td>0.191</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.0706</td>\n",
+       "      <td>IGFALS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P02743</th>\n",
+       "      <td>0.15</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>APCS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A0A0G2JMB2</th>\n",
+       "      <td>0.141</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>IGHA2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>O00391</th>\n",
+       "      <td>0.138</td>\n",
+       "      <td>0.107</td>\n",
+       "      <td>-</td>\n",
+       "      <td>QSOX1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q08380</th>\n",
+       "      <td>0.131</td>\n",
+       "      <td>0.177</td>\n",
+       "      <td>0.092</td>\n",
+       "      <td>LGALS3BP</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P01833</th>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.122</td>\n",
+       "      <td>-</td>\n",
+       "      <td>PIGR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P00739</th>\n",
+       "      <td>0.124</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>HPR</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               F2     I2      S1 Gene Name\n",
+       "Protein ID                                \n",
+       "P10643      0.263  0.148       -        C7\n",
+       "P19320      0.201      -   0.085     VCAM1\n",
+       "Q16270      0.193  0.121       -    IGFBP7\n",
+       "P35858      0.191      -  0.0706    IGFALS\n",
+       "P02743       0.15      -       -      APCS\n",
+       "A0A0G2JMB2  0.141      -       -     IGHA2\n",
+       "O00391      0.138  0.107       -     QSOX1\n",
+       "Q08380      0.131  0.177   0.092  LGALS3BP\n",
+       "P01833      0.125  0.122       -      PIGR\n",
+       "P00739      0.124      -       -       HPR"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "pd.set_option('precision', 3)\n",
     "display(df_protein_panel.head(10))"
@@ -2334,18 +8364,406 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 243,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>F2</th>\n",
+       "      <th>I2</th>\n",
+       "      <th>S1</th>\n",
+       "      <th>Gene Name</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Protein ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>P10643</th>\n",
+       "      <td>0.263</td>\n",
+       "      <td>0.148</td>\n",
+       "      <td>-</td>\n",
+       "      <td>C7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P19320</th>\n",
+       "      <td>0.201</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.085</td>\n",
+       "      <td>VCAM1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q16270</th>\n",
+       "      <td>0.193</td>\n",
+       "      <td>0.121</td>\n",
+       "      <td>-</td>\n",
+       "      <td>IGFBP7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P35858</th>\n",
+       "      <td>0.191</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.0706</td>\n",
+       "      <td>IGFALS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P02743</th>\n",
+       "      <td>0.15</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>APCS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A0A0G2JMB2</th>\n",
+       "      <td>0.141</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>IGHA2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>O00391</th>\n",
+       "      <td>0.138</td>\n",
+       "      <td>0.107</td>\n",
+       "      <td>-</td>\n",
+       "      <td>QSOX1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q08380</th>\n",
+       "      <td>0.131</td>\n",
+       "      <td>0.177</td>\n",
+       "      <td>0.092</td>\n",
+       "      <td>LGALS3BP</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P01833</th>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.122</td>\n",
+       "      <td>-</td>\n",
+       "      <td>PIGR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P00739</th>\n",
+       "      <td>0.124</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>HPR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A0A0A0MRZ8</th>\n",
+       "      <td>0.121</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>IGKV3D-11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q99650</th>\n",
+       "      <td>0.121</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>OSMR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q9Y5Y7</th>\n",
+       "      <td>0.116</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.0529</td>\n",
+       "      <td>LYVE1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q15582</th>\n",
+       "      <td>0.11</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.0836</td>\n",
+       "      <td>TGFBI</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P10909</th>\n",
+       "      <td>-</td>\n",
+       "      <td>0.097</td>\n",
+       "      <td>-</td>\n",
+       "      <td>CLU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P23142</th>\n",
+       "      <td>-</td>\n",
+       "      <td>0.0908</td>\n",
+       "      <td>-</td>\n",
+       "      <td>FBLN1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P05362</th>\n",
+       "      <td>-</td>\n",
+       "      <td>0.0865</td>\n",
+       "      <td>-</td>\n",
+       "      <td>ICAM1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P05546</th>\n",
+       "      <td>-</td>\n",
+       "      <td>0.0851</td>\n",
+       "      <td>-</td>\n",
+       "      <td>SERPIND1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P05062</th>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.177</td>\n",
+       "      <td>ALDOB</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>H7BY64</th>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.0916</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P06396</th>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.0904</td>\n",
+       "      <td>GSN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C9JPQ9</th>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.0866</td>\n",
+       "      <td>FGG</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P55103</th>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.0825</td>\n",
+       "      <td>INHBC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P08519</th>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.075</td>\n",
+       "      <td>LPA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q92820</th>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.0724</td>\n",
+       "      <td>GGH</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q92496</th>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.0617</td>\n",
+       "      <td>CFHR4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P12955</th>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.0602</td>\n",
+       "      <td>PEPD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q9UGM5</th>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.0581</td>\n",
+       "      <td>FETUB</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P02774</th>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.0573</td>\n",
+       "      <td>GC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P33151</th>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.0569</td>\n",
+       "      <td>CDH5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q9UJJ9</th>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.0566</td>\n",
+       "      <td>GNPTG</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q14624</th>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.0559</td>\n",
+       "      <td>ITIH4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P43652</th>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.055</td>\n",
+       "      <td>AFM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P04196</th>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.0533</td>\n",
+       "      <td>HRG</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B0YIW2</th>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.0509</td>\n",
+       "      <td>APOC3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P80748</th>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.0506</td>\n",
+       "      <td>IGLV3-21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A0A0U1RR20</th>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.049</td>\n",
+       "      <td>PRG4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P09172</th>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.0487</td>\n",
+       "      <td>DBH</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P01009</th>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.045</td>\n",
+       "      <td>SERPINA1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P0C0L5</th>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.0446</td>\n",
+       "      <td>C4B</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A0A182DWH7</th>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0.0435</td>\n",
+       "      <td>SELENOP</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               F2      I2      S1  Gene Name\n",
+       "Protein ID                                  \n",
+       "P10643      0.263   0.148       -         C7\n",
+       "P19320      0.201       -   0.085      VCAM1\n",
+       "Q16270      0.193   0.121       -     IGFBP7\n",
+       "P35858      0.191       -  0.0706     IGFALS\n",
+       "P02743       0.15       -       -       APCS\n",
+       "A0A0G2JMB2  0.141       -       -      IGHA2\n",
+       "O00391      0.138   0.107       -      QSOX1\n",
+       "Q08380      0.131   0.177   0.092   LGALS3BP\n",
+       "P01833      0.125   0.122       -       PIGR\n",
+       "P00739      0.124       -       -        HPR\n",
+       "A0A0A0MRZ8  0.121       -       -  IGKV3D-11\n",
+       "Q99650      0.121       -       -       OSMR\n",
+       "Q9Y5Y7      0.116       -  0.0529      LYVE1\n",
+       "Q15582       0.11       -  0.0836      TGFBI\n",
+       "P10909          -   0.097       -        CLU\n",
+       "P23142          -  0.0908       -      FBLN1\n",
+       "P05362          -  0.0865       -      ICAM1\n",
+       "P05546          -  0.0851       -   SERPIND1\n",
+       "P05062          -       -   0.177      ALDOB\n",
+       "H7BY64          -       -  0.0916        NaN\n",
+       "P06396          -       -  0.0904        GSN\n",
+       "C9JPQ9          -       -  0.0866        FGG\n",
+       "P55103          -       -  0.0825      INHBC\n",
+       "P08519          -       -   0.075        LPA\n",
+       "Q92820          -       -  0.0724        GGH\n",
+       "Q92496          -       -  0.0617      CFHR4\n",
+       "P12955          -       -  0.0602       PEPD\n",
+       "Q9UGM5          -       -  0.0581      FETUB\n",
+       "P02774          -       -  0.0573         GC\n",
+       "P33151          -       -  0.0569       CDH5\n",
+       "Q9UJJ9          -       -  0.0566      GNPTG\n",
+       "Q14624          -       -  0.0559      ITIH4\n",
+       "P43652          -       -   0.055        AFM\n",
+       "P04196          -       -  0.0533        HRG\n",
+       "B0YIW2          -       -  0.0509      APOC3\n",
+       "P80748          -       -  0.0506   IGLV3-21\n",
+       "A0A0U1RR20      -       -   0.049       PRG4\n",
+       "P09172          -       -  0.0487        DBH\n",
+       "P01009          -       -   0.045   SERPINA1\n",
+       "P0C0L5          -       -  0.0446        C4B\n",
+       "A0A182DWH7      -       -  0.0435    SELENOP"
+      ]
+     },
+     "execution_count": 243,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df_protein_panel"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 244,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "41"
+      ]
+     },
+     "execution_count": 244,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "len(df_protein_panel)"
    ]
@@ -2359,7 +8777,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 245,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2368,7 +8786,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 246,
    "metadata": {
     "tags": []
    },
@@ -2423,9 +8841,134 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 247,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>fit_time</th>\n",
+       "      <th>score_time</th>\n",
+       "      <th>test_precision</th>\n",
+       "      <th>test_recall</th>\n",
+       "      <th>test_f1</th>\n",
+       "      <th>test_balanced_accuracy</th>\n",
+       "      <th>test_roc_auc</th>\n",
+       "      <th>n_observations</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>test_case</th>\n",
+       "      <th>n_features</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>S1</th>\n",
+       "      <th>49</th>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>0.841</td>\n",
+       "      <td>0.853</td>\n",
+       "      <td>0.846</td>\n",
+       "      <td>0.822</td>\n",
+       "      <td>0.905</td>\n",
+       "      <td>350</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2</th>\n",
+       "      <th>11</th>\n",
+       "      <td>0.003</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>0.856</td>\n",
+       "      <td>0.817</td>\n",
+       "      <td>0.834</td>\n",
+       "      <td>0.821</td>\n",
+       "      <td>0.905</td>\n",
+       "      <td>358</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3</th>\n",
+       "      <th>5</th>\n",
+       "      <td>0.003</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>0.879</td>\n",
+       "      <td>0.783</td>\n",
+       "      <td>0.825</td>\n",
+       "      <td>0.872</td>\n",
+       "      <td>0.963</td>\n",
+       "      <td>358</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I2</th>\n",
+       "      <th>6</th>\n",
+       "      <td>0.003</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>0.796</td>\n",
+       "      <td>0.791</td>\n",
+       "      <td>0.792</td>\n",
+       "      <td>0.777</td>\n",
+       "      <td>0.854</td>\n",
+       "      <td>350</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      fit_time  score_time  test_precision  test_recall  \\\n",
+       "test_case n_features                                                      \n",
+       "S1        49             0.005       0.008           0.841        0.853   \n",
+       "F2        11             0.003       0.006           0.856        0.817   \n",
+       "F3        5              0.003       0.007           0.879        0.783   \n",
+       "I2        6              0.003       0.007           0.796        0.791   \n",
+       "\n",
+       "                      test_f1  test_balanced_accuracy  test_roc_auc  \\\n",
+       "test_case n_features                                                  \n",
+       "S1        49            0.846                   0.822         0.905   \n",
+       "F2        11            0.834                   0.821         0.905   \n",
+       "F3        5             0.825                   0.872         0.963   \n",
+       "I2        6             0.792                   0.777         0.854   \n",
+       "\n",
+       "                      n_observations  \n",
+       "test_case n_features                  \n",
+       "S1        49                     350  \n",
+       "F2        11                     358  \n",
+       "F3        5                      358  \n",
+       "I2        6                      350  "
+      ]
+     },
+     "execution_count": 247,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "combined_mrmr = summary_n_features_mrmr.groupby(['test_case','n_features']).mean()\n",
     "\n",
@@ -2435,9 +8978,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 248,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
+       " [Text(0, 0, ''),\n",
+       "  Text(0, 0, ''),\n",
+       "  Text(0, 0, ''),\n",
+       "  Text(0, 0, ''),\n",
+       "  Text(0, 0, ''),\n",
+       "  Text(0, 0, '')])"
+      ]
+     },
+     "execution_count": 248,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(5,5))\n",
     "sns.lineplot(x='n_features',y='test_roc_auc',hue='test_case', data=summary_n_features_mrmr)\n",
@@ -2470,18 +9042,142 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 249,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>F2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Protein ID</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>P10643</th>\n",
+       "      <td>C7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P19320</th>\n",
+       "      <td>VCAM1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q16270</th>\n",
+       "      <td>IGFBP7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P35858</th>\n",
+       "      <td>IGFALS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P02743</th>\n",
+       "      <td>APCS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A0A0G2JMB2</th>\n",
+       "      <td>IGHA2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>O00391</th>\n",
+       "      <td>QSOX1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q08380</th>\n",
+       "      <td>LGALS3BP</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P01833</th>\n",
+       "      <td>PIGR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P00739</th>\n",
+       "      <td>HPR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A0A0A0MRZ8</th>\n",
+       "      <td>IGKV3D-11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q99650</th>\n",
+       "      <td>OSMR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q9Y5Y7</th>\n",
+       "      <td>LYVE1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q15582</th>\n",
+       "      <td>TGFBI</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   F2\n",
+       "Protein ID           \n",
+       "P10643             C7\n",
+       "P19320          VCAM1\n",
+       "Q16270         IGFBP7\n",
+       "P35858         IGFALS\n",
+       "P02743           APCS\n",
+       "A0A0G2JMB2      IGHA2\n",
+       "O00391          QSOX1\n",
+       "Q08380       LGALS3BP\n",
+       "P01833           PIGR\n",
+       "P00739            HPR\n",
+       "A0A0A0MRZ8  IGKV3D-11\n",
+       "Q99650           OSMR\n",
+       "Q9Y5Y7          LYVE1\n",
+       "Q15582          TGFBI"
+      ]
+     },
+     "execution_count": 249,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "proteins_selected_f2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 250,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 9/9 [00:00<00:00, 16.27it/s]\n",
+      "100%|██████████| 18/18 [00:01<00:00, 14.12it/s]\n",
+      "100%|██████████| 8/8 [00:00<00:00, 14.42it/s]\n",
+      "100%|██████████| 20/20 [00:01<00:00, 15.23it/s]\n"
+     ]
+    }
+   ],
    "source": [
     "test_cases = {}\n",
     "test_cases['F2'] = {'n_features': 9, 'y':kleiner_ge_2}\n",
@@ -2503,9 +9199,152 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 251,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>I2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Protein ID</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Q08380</th>\n",
+       "      <td>LGALS3BP</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P04196</th>\n",
+       "      <td>HRG</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P10643</th>\n",
+       "      <td>C7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q15582</th>\n",
+       "      <td>TGFBI</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A0A0A0MRJ7</th>\n",
+       "      <td>F5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P35858</th>\n",
+       "      <td>IGFALS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P01833</th>\n",
+       "      <td>PIGR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q16270</th>\n",
+       "      <td>IGFBP7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>O00391</th>\n",
+       "      <td>QSOX1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P25311</th>\n",
+       "      <td>AZGP1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P05362</th>\n",
+       "      <td>ICAM1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P05062</th>\n",
+       "      <td>ALDOB</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P02768</th>\n",
+       "      <td>ALB</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E9PEP6</th>\n",
+       "      <td>NRP1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P01008</th>\n",
+       "      <td>SERPINC1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>O75636</th>\n",
+       "      <td>FCN3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>E9PHK0</th>\n",
+       "      <td>CLEC3B</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P02743</th>\n",
+       "      <td>APCS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P43652</th>\n",
+       "      <td>AFM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P19320</th>\n",
+       "      <td>VCAM1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  I2\n",
+       "Protein ID          \n",
+       "Q08380      LGALS3BP\n",
+       "P04196           HRG\n",
+       "P10643            C7\n",
+       "Q15582         TGFBI\n",
+       "A0A0A0MRJ7        F5\n",
+       "P35858        IGFALS\n",
+       "P01833          PIGR\n",
+       "Q16270        IGFBP7\n",
+       "O00391         QSOX1\n",
+       "P25311         AZGP1\n",
+       "P05362         ICAM1\n",
+       "P05062         ALDOB\n",
+       "P02768           ALB\n",
+       "E9PEP6          NRP1\n",
+       "P01008      SERPINC1\n",
+       "O75636          FCN3\n",
+       "E9PHK0        CLEC3B\n",
+       "P02743          APCS\n",
+       "P43652           AFM\n",
+       "P19320         VCAM1"
+      ]
+     },
+     "execution_count": 251,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "mrmr_markers['I2']"
    ]
@@ -2523,7 +9362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 252,
    "metadata": {
     "Collapsed": "false"
    },
@@ -2553,7 +9392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 253,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2582,22 +9421,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 254,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    200\n",
+       "0    160\n",
+       "Name: kleiner, dtype: int64"
+      ]
+     },
+     "execution_count": 254,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kleiner_ge_2.value_counts(dropna=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 255,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'te': 7.0, 'swe': 8.6, 'elf': 7.7, 'ft': 0.48, 'fib4': 1.45, 'apri': 0.5}"
+      ]
+     },
+     "execution_count": 255,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cutoffs_f2 = cutoffs_clinic['F2'].dropna().to_dict()\n",
     "cutoffs_f2"
@@ -2605,11 +9468,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 256,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    360.000\n",
+       "mean       0.556\n",
+       "std        0.498\n",
+       "min        0.000\n",
+       "25%        0.000\n",
+       "50%        1.000\n",
+       "75%        1.000\n",
+       "max        1.000\n",
+       "Name: kleiner, dtype: float64"
+      ]
+     },
+     "execution_count": 256,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "y = kleiner_ge_2.astype(int)\n",
     "y.describe()"
@@ -2617,11 +9499,412 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 257,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>variable</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">precision</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">recall</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">f1</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">balanced_accuracy</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">roc_auc</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">num_feat</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">n_obs</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">roc_auc_2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>statistics</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>F2_prot_Logistic</th>\n",
+       "      <td>0.845</td>\n",
+       "      <td>0.062</td>\n",
+       "      <td>0.800</td>\n",
+       "      <td>0.054</td>\n",
+       "      <td>0.820</td>\n",
+       "      <td>0.042</td>\n",
+       "      <td>0.808</td>\n",
+       "      <td>0.039</td>\n",
+       "      <td>0.884</td>\n",
+       "      <td>0.034</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>358.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.884</td>\n",
+       "      <td>0.034</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_elf_Logistic</th>\n",
+       "      <td>0.766</td>\n",
+       "      <td>0.083</td>\n",
+       "      <td>0.823</td>\n",
+       "      <td>0.048</td>\n",
+       "      <td>0.790</td>\n",
+       "      <td>0.045</td>\n",
+       "      <td>0.755</td>\n",
+       "      <td>0.046</td>\n",
+       "      <td>0.851</td>\n",
+       "      <td>0.034</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>349.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.851</td>\n",
+       "      <td>0.034</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_te</th>\n",
+       "      <td>0.760</td>\n",
+       "      <td>0.068</td>\n",
+       "      <td>0.821</td>\n",
+       "      <td>0.051</td>\n",
+       "      <td>0.788</td>\n",
+       "      <td>0.047</td>\n",
+       "      <td>0.757</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>0.757</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>341.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.757</td>\n",
+       "      <td>0.040</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_swe</th>\n",
+       "      <td>0.795</td>\n",
+       "      <td>0.054</td>\n",
+       "      <td>0.776</td>\n",
+       "      <td>0.055</td>\n",
+       "      <td>0.784</td>\n",
+       "      <td>0.041</td>\n",
+       "      <td>0.768</td>\n",
+       "      <td>0.036</td>\n",
+       "      <td>0.768</td>\n",
+       "      <td>0.036</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>331.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.768</td>\n",
+       "      <td>0.036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_te_Logistic</th>\n",
+       "      <td>0.848</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>0.734</td>\n",
+       "      <td>0.073</td>\n",
+       "      <td>0.783</td>\n",
+       "      <td>0.050</td>\n",
+       "      <td>0.789</td>\n",
+       "      <td>0.041</td>\n",
+       "      <td>0.873</td>\n",
+       "      <td>0.033</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>341.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.873</td>\n",
+       "      <td>0.033</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_ft_Logistic</th>\n",
+       "      <td>0.844</td>\n",
+       "      <td>0.074</td>\n",
+       "      <td>0.735</td>\n",
+       "      <td>0.071</td>\n",
+       "      <td>0.782</td>\n",
+       "      <td>0.052</td>\n",
+       "      <td>0.791</td>\n",
+       "      <td>0.045</td>\n",
+       "      <td>0.867</td>\n",
+       "      <td>0.035</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>268.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.867</td>\n",
+       "      <td>0.035</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_swe_Logistic</th>\n",
+       "      <td>0.857</td>\n",
+       "      <td>0.057</td>\n",
+       "      <td>0.711</td>\n",
+       "      <td>0.065</td>\n",
+       "      <td>0.775</td>\n",
+       "      <td>0.047</td>\n",
+       "      <td>0.785</td>\n",
+       "      <td>0.037</td>\n",
+       "      <td>0.881</td>\n",
+       "      <td>0.029</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>331.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.881</td>\n",
+       "      <td>0.029</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_forns_Logistic</th>\n",
+       "      <td>0.721</td>\n",
+       "      <td>0.064</td>\n",
+       "      <td>0.772</td>\n",
+       "      <td>0.062</td>\n",
+       "      <td>0.743</td>\n",
+       "      <td>0.041</td>\n",
+       "      <td>0.701</td>\n",
+       "      <td>0.034</td>\n",
+       "      <td>0.809</td>\n",
+       "      <td>0.033</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>356.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.809</td>\n",
+       "      <td>0.033</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_fib4</th>\n",
+       "      <td>0.743</td>\n",
+       "      <td>0.058</td>\n",
+       "      <td>0.744</td>\n",
+       "      <td>0.051</td>\n",
+       "      <td>0.742</td>\n",
+       "      <td>0.044</td>\n",
+       "      <td>0.711</td>\n",
+       "      <td>0.038</td>\n",
+       "      <td>0.711</td>\n",
+       "      <td>0.038</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>352.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.711</td>\n",
+       "      <td>0.038</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_p3np_Logistic</th>\n",
+       "      <td>0.819</td>\n",
+       "      <td>0.071</td>\n",
+       "      <td>0.665</td>\n",
+       "      <td>0.068</td>\n",
+       "      <td>0.731</td>\n",
+       "      <td>0.051</td>\n",
+       "      <td>0.747</td>\n",
+       "      <td>0.044</td>\n",
+       "      <td>0.804</td>\n",
+       "      <td>0.039</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>319.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.804</td>\n",
+       "      <td>0.039</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_elf</th>\n",
+       "      <td>0.573</td>\n",
+       "      <td>0.057</td>\n",
+       "      <td>0.995</td>\n",
+       "      <td>0.010</td>\n",
+       "      <td>0.725</td>\n",
+       "      <td>0.047</td>\n",
+       "      <td>0.533</td>\n",
+       "      <td>0.017</td>\n",
+       "      <td>0.533</td>\n",
+       "      <td>0.017</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>349.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.533</td>\n",
+       "      <td>0.017</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_fib4_Logistic</th>\n",
+       "      <td>0.789</td>\n",
+       "      <td>0.082</td>\n",
+       "      <td>0.674</td>\n",
+       "      <td>0.059</td>\n",
+       "      <td>0.723</td>\n",
+       "      <td>0.046</td>\n",
+       "      <td>0.724</td>\n",
+       "      <td>0.043</td>\n",
+       "      <td>0.788</td>\n",
+       "      <td>0.043</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>352.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.788</td>\n",
+       "      <td>0.043</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_apri_Logistic</th>\n",
+       "      <td>0.786</td>\n",
+       "      <td>0.089</td>\n",
+       "      <td>0.669</td>\n",
+       "      <td>0.056</td>\n",
+       "      <td>0.718</td>\n",
+       "      <td>0.043</td>\n",
+       "      <td>0.721</td>\n",
+       "      <td>0.042</td>\n",
+       "      <td>0.766</td>\n",
+       "      <td>0.043</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.766</td>\n",
+       "      <td>0.043</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_apri</th>\n",
+       "      <td>0.835</td>\n",
+       "      <td>0.079</td>\n",
+       "      <td>0.564</td>\n",
+       "      <td>0.064</td>\n",
+       "      <td>0.670</td>\n",
+       "      <td>0.058</td>\n",
+       "      <td>0.712</td>\n",
+       "      <td>0.043</td>\n",
+       "      <td>0.712</td>\n",
+       "      <td>0.043</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.712</td>\n",
+       "      <td>0.043</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_ft</th>\n",
+       "      <td>0.910</td>\n",
+       "      <td>0.065</td>\n",
+       "      <td>0.493</td>\n",
+       "      <td>0.069</td>\n",
+       "      <td>0.637</td>\n",
+       "      <td>0.063</td>\n",
+       "      <td>0.719</td>\n",
+       "      <td>0.039</td>\n",
+       "      <td>0.719</td>\n",
+       "      <td>0.039</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>268.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.719</td>\n",
+       "      <td>0.039</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "variable                 precision        recall            f1         \\\n",
+       "statistics                    mean    std   mean    std   mean    std   \n",
+       "F2_prot_Logistic             0.845  0.062  0.800  0.054  0.820  0.042   \n",
+       "F2_marker_elf_Logistic       0.766  0.083  0.823  0.048  0.790  0.045   \n",
+       "F2_marker_te                 0.760  0.068  0.821  0.051  0.788  0.047   \n",
+       "F2_marker_swe                0.795  0.054  0.776  0.055  0.784  0.041   \n",
+       "F2_marker_te_Logistic        0.848  0.076  0.734  0.073  0.783  0.050   \n",
+       "F2_marker_ft_Logistic        0.844  0.074  0.735  0.071  0.782  0.052   \n",
+       "F2_marker_swe_Logistic       0.857  0.057  0.711  0.065  0.775  0.047   \n",
+       "F2_marker_forns_Logistic     0.721  0.064  0.772  0.062  0.743  0.041   \n",
+       "F2_marker_fib4               0.743  0.058  0.744  0.051  0.742  0.044   \n",
+       "F2_marker_p3np_Logistic      0.819  0.071  0.665  0.068  0.731  0.051   \n",
+       "F2_marker_elf                0.573  0.057  0.995  0.010  0.725  0.047   \n",
+       "F2_marker_fib4_Logistic      0.789  0.082  0.674  0.059  0.723  0.046   \n",
+       "F2_marker_apri_Logistic      0.786  0.089  0.669  0.056  0.718  0.043   \n",
+       "F2_marker_apri               0.835  0.079  0.564  0.064  0.670  0.058   \n",
+       "F2_marker_ft                 0.910  0.065  0.493  0.069  0.637  0.063   \n",
+       "\n",
+       "variable                 balanced_accuracy        roc_auc        num_feat  \\\n",
+       "statistics                            mean    std    mean    std     mean   \n",
+       "F2_prot_Logistic                     0.808  0.039   0.884  0.034     14.0   \n",
+       "F2_marker_elf_Logistic               0.755  0.046   0.851  0.034      1.0   \n",
+       "F2_marker_te                         0.757  0.040   0.757  0.040      1.0   \n",
+       "F2_marker_swe                        0.768  0.036   0.768  0.036      1.0   \n",
+       "F2_marker_te_Logistic                0.789  0.041   0.873  0.033      1.0   \n",
+       "F2_marker_ft_Logistic                0.791  0.045   0.867  0.035      1.0   \n",
+       "F2_marker_swe_Logistic               0.785  0.037   0.881  0.029      1.0   \n",
+       "F2_marker_forns_Logistic             0.701  0.034   0.809  0.033      1.0   \n",
+       "F2_marker_fib4                       0.711  0.038   0.711  0.038      1.0   \n",
+       "F2_marker_p3np_Logistic              0.747  0.044   0.804  0.039      1.0   \n",
+       "F2_marker_elf                        0.533  0.017   0.533  0.017      1.0   \n",
+       "F2_marker_fib4_Logistic              0.724  0.043   0.788  0.043      1.0   \n",
+       "F2_marker_apri_Logistic              0.721  0.042   0.766  0.043      1.0   \n",
+       "F2_marker_apri                       0.712  0.043   0.712  0.043      1.0   \n",
+       "F2_marker_ft                         0.719  0.039   0.719  0.039      1.0   \n",
+       "\n",
+       "variable                       n_obs      roc_auc_2         \n",
+       "statistics                std   mean  std      mean    std  \n",
+       "F2_prot_Logistic          0.0  358.0  0.0     0.884  0.034  \n",
+       "F2_marker_elf_Logistic    0.0  349.0  0.0     0.851  0.034  \n",
+       "F2_marker_te              0.0  341.0  0.0     0.757  0.040  \n",
+       "F2_marker_swe             0.0  331.0  0.0     0.768  0.036  \n",
+       "F2_marker_te_Logistic     0.0  341.0  0.0     0.873  0.033  \n",
+       "F2_marker_ft_Logistic     0.0  268.0  0.0     0.867  0.035  \n",
+       "F2_marker_swe_Logistic    0.0  331.0  0.0     0.881  0.029  \n",
+       "F2_marker_forns_Logistic  0.0  356.0  0.0     0.809  0.033  \n",
+       "F2_marker_fib4            0.0  352.0  0.0     0.711  0.038  \n",
+       "F2_marker_p3np_Logistic   0.0  319.0  0.0     0.804  0.039  \n",
+       "F2_marker_elf             0.0  349.0  0.0     0.533  0.017  \n",
+       "F2_marker_fib4_Logistic   0.0  352.0  0.0     0.788  0.043  \n",
+       "F2_marker_apri_Logistic   0.0  353.0  0.0     0.766  0.043  \n",
+       "F2_marker_apri            0.0  353.0  0.0     0.712  0.043  \n",
+       "F2_marker_ft              0.0  268.0  0.0     0.719  0.039  "
+      ]
+     },
+     "execution_count": 257,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#proteins_selected = mrmr_markers['F2']\n",
     "proteins_selected = proteins_selected_f2\n",
@@ -2639,7 +9922,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 258,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2657,22 +9940,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 259,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    266\n",
+       "1     94\n",
+       "Name: kleiner, dtype: int64"
+      ]
+     },
+     "execution_count": 259,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kleiner_ge_3.value_counts(dropna=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 260,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'te': 15.0,\n",
+       " 'swe': 16.4,\n",
+       " 'elf': 10.5,\n",
+       " 'ft': 0.58,\n",
+       " 'fib4': 3.25,\n",
+       " 'apri': 1.0,\n",
+       " 'forns': 6.8}"
+      ]
+     },
+     "execution_count": 260,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cutoffs_f3 = cutoffs_clinic['F3'].dropna().to_dict()\n",
     "cutoffs_f3"
@@ -2689,11 +10002,434 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 261,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>variable</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">precision</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">recall</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">f1</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">balanced_accuracy</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">roc_auc</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">num_feat</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">n_obs</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">roc_auc_2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>statistics</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_swe</th>\n",
+       "      <td>0.857</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>0.833</td>\n",
+       "      <td>0.093</td>\n",
+       "      <td>0.840</td>\n",
+       "      <td>0.058</td>\n",
+       "      <td>0.892</td>\n",
+       "      <td>0.046</td>\n",
+       "      <td>0.892</td>\n",
+       "      <td>0.046</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>331.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.892</td>\n",
+       "      <td>0.046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_te</th>\n",
+       "      <td>0.792</td>\n",
+       "      <td>0.080</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.072</td>\n",
+       "      <td>0.836</td>\n",
+       "      <td>0.058</td>\n",
+       "      <td>0.907</td>\n",
+       "      <td>0.038</td>\n",
+       "      <td>0.907</td>\n",
+       "      <td>0.038</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>341.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.907</td>\n",
+       "      <td>0.038</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_prot_Logistic</th>\n",
+       "      <td>0.873</td>\n",
+       "      <td>0.066</td>\n",
+       "      <td>0.783</td>\n",
+       "      <td>0.084</td>\n",
+       "      <td>0.822</td>\n",
+       "      <td>0.059</td>\n",
+       "      <td>0.871</td>\n",
+       "      <td>0.044</td>\n",
+       "      <td>0.960</td>\n",
+       "      <td>0.019</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>358.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.960</td>\n",
+       "      <td>0.019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_swe_Logistic</th>\n",
+       "      <td>0.869</td>\n",
+       "      <td>0.080</td>\n",
+       "      <td>0.689</td>\n",
+       "      <td>0.134</td>\n",
+       "      <td>0.758</td>\n",
+       "      <td>0.087</td>\n",
+       "      <td>0.826</td>\n",
+       "      <td>0.064</td>\n",
+       "      <td>0.955</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>331.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.955</td>\n",
+       "      <td>0.025</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_elf</th>\n",
+       "      <td>0.700</td>\n",
+       "      <td>0.088</td>\n",
+       "      <td>0.752</td>\n",
+       "      <td>0.092</td>\n",
+       "      <td>0.722</td>\n",
+       "      <td>0.073</td>\n",
+       "      <td>0.820</td>\n",
+       "      <td>0.049</td>\n",
+       "      <td>0.820</td>\n",
+       "      <td>0.049</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>349.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.820</td>\n",
+       "      <td>0.049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_te_Logistic</th>\n",
+       "      <td>0.845</td>\n",
+       "      <td>0.088</td>\n",
+       "      <td>0.635</td>\n",
+       "      <td>0.105</td>\n",
+       "      <td>0.717</td>\n",
+       "      <td>0.074</td>\n",
+       "      <td>0.798</td>\n",
+       "      <td>0.050</td>\n",
+       "      <td>0.955</td>\n",
+       "      <td>0.019</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>341.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.955</td>\n",
+       "      <td>0.019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_forns</th>\n",
+       "      <td>0.641</td>\n",
+       "      <td>0.082</td>\n",
+       "      <td>0.703</td>\n",
+       "      <td>0.097</td>\n",
+       "      <td>0.667</td>\n",
+       "      <td>0.077</td>\n",
+       "      <td>0.780</td>\n",
+       "      <td>0.053</td>\n",
+       "      <td>0.780</td>\n",
+       "      <td>0.053</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>356.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.780</td>\n",
+       "      <td>0.053</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_ft</th>\n",
+       "      <td>0.645</td>\n",
+       "      <td>0.113</td>\n",
+       "      <td>0.638</td>\n",
+       "      <td>0.126</td>\n",
+       "      <td>0.635</td>\n",
+       "      <td>0.102</td>\n",
+       "      <td>0.762</td>\n",
+       "      <td>0.069</td>\n",
+       "      <td>0.762</td>\n",
+       "      <td>0.069</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>268.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.762</td>\n",
+       "      <td>0.069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_ft_Logistic</th>\n",
+       "      <td>0.745</td>\n",
+       "      <td>0.112</td>\n",
+       "      <td>0.551</td>\n",
+       "      <td>0.149</td>\n",
+       "      <td>0.622</td>\n",
+       "      <td>0.123</td>\n",
+       "      <td>0.745</td>\n",
+       "      <td>0.075</td>\n",
+       "      <td>0.905</td>\n",
+       "      <td>0.039</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>268.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.905</td>\n",
+       "      <td>0.039</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_fib4</th>\n",
+       "      <td>0.688</td>\n",
+       "      <td>0.098</td>\n",
+       "      <td>0.574</td>\n",
+       "      <td>0.104</td>\n",
+       "      <td>0.620</td>\n",
+       "      <td>0.087</td>\n",
+       "      <td>0.741</td>\n",
+       "      <td>0.053</td>\n",
+       "      <td>0.741</td>\n",
+       "      <td>0.053</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>352.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.741</td>\n",
+       "      <td>0.053</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_forns_Logistic</th>\n",
+       "      <td>0.766</td>\n",
+       "      <td>0.121</td>\n",
+       "      <td>0.485</td>\n",
+       "      <td>0.116</td>\n",
+       "      <td>0.585</td>\n",
+       "      <td>0.105</td>\n",
+       "      <td>0.715</td>\n",
+       "      <td>0.059</td>\n",
+       "      <td>0.842</td>\n",
+       "      <td>0.055</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>356.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.842</td>\n",
+       "      <td>0.055</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_p3np_Logistic</th>\n",
+       "      <td>0.745</td>\n",
+       "      <td>0.129</td>\n",
+       "      <td>0.425</td>\n",
+       "      <td>0.120</td>\n",
+       "      <td>0.531</td>\n",
+       "      <td>0.112</td>\n",
+       "      <td>0.690</td>\n",
+       "      <td>0.059</td>\n",
+       "      <td>0.840</td>\n",
+       "      <td>0.049</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>319.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.840</td>\n",
+       "      <td>0.049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_elf_Logistic</th>\n",
+       "      <td>0.914</td>\n",
+       "      <td>0.097</td>\n",
+       "      <td>0.380</td>\n",
+       "      <td>0.116</td>\n",
+       "      <td>0.524</td>\n",
+       "      <td>0.117</td>\n",
+       "      <td>0.684</td>\n",
+       "      <td>0.056</td>\n",
+       "      <td>0.908</td>\n",
+       "      <td>0.033</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>349.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.908</td>\n",
+       "      <td>0.033</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_apri</th>\n",
+       "      <td>0.512</td>\n",
+       "      <td>0.114</td>\n",
+       "      <td>0.367</td>\n",
+       "      <td>0.104</td>\n",
+       "      <td>0.421</td>\n",
+       "      <td>0.098</td>\n",
+       "      <td>0.622</td>\n",
+       "      <td>0.052</td>\n",
+       "      <td>0.622</td>\n",
+       "      <td>0.052</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.622</td>\n",
+       "      <td>0.052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_fib4_Logistic</th>\n",
+       "      <td>0.706</td>\n",
+       "      <td>0.169</td>\n",
+       "      <td>0.232</td>\n",
+       "      <td>0.103</td>\n",
+       "      <td>0.334</td>\n",
+       "      <td>0.111</td>\n",
+       "      <td>0.596</td>\n",
+       "      <td>0.046</td>\n",
+       "      <td>0.836</td>\n",
+       "      <td>0.052</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>352.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.836</td>\n",
+       "      <td>0.052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_apri_Logistic</th>\n",
+       "      <td>0.391</td>\n",
+       "      <td>0.354</td>\n",
+       "      <td>0.065</td>\n",
+       "      <td>0.069</td>\n",
+       "      <td>0.104</td>\n",
+       "      <td>0.101</td>\n",
+       "      <td>0.520</td>\n",
+       "      <td>0.030</td>\n",
+       "      <td>0.782</td>\n",
+       "      <td>0.055</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.782</td>\n",
+       "      <td>0.055</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "variable                 precision        recall            f1         \\\n",
+       "statistics                    mean    std   mean    std   mean    std   \n",
+       "F3_marker_swe                0.857  0.076  0.833  0.093  0.840  0.058   \n",
+       "F3_marker_te                 0.792  0.080  0.893  0.072  0.836  0.058   \n",
+       "F3_prot_Logistic             0.873  0.066  0.783  0.084  0.822  0.059   \n",
+       "F3_marker_swe_Logistic       0.869  0.080  0.689  0.134  0.758  0.087   \n",
+       "F3_marker_elf                0.700  0.088  0.752  0.092  0.722  0.073   \n",
+       "F3_marker_te_Logistic        0.845  0.088  0.635  0.105  0.717  0.074   \n",
+       "F3_marker_forns              0.641  0.082  0.703  0.097  0.667  0.077   \n",
+       "F3_marker_ft                 0.645  0.113  0.638  0.126  0.635  0.102   \n",
+       "F3_marker_ft_Logistic        0.745  0.112  0.551  0.149  0.622  0.123   \n",
+       "F3_marker_fib4               0.688  0.098  0.574  0.104  0.620  0.087   \n",
+       "F3_marker_forns_Logistic     0.766  0.121  0.485  0.116  0.585  0.105   \n",
+       "F3_marker_p3np_Logistic      0.745  0.129  0.425  0.120  0.531  0.112   \n",
+       "F3_marker_elf_Logistic       0.914  0.097  0.380  0.116  0.524  0.117   \n",
+       "F3_marker_apri               0.512  0.114  0.367  0.104  0.421  0.098   \n",
+       "F3_marker_fib4_Logistic      0.706  0.169  0.232  0.103  0.334  0.111   \n",
+       "F3_marker_apri_Logistic      0.391  0.354  0.065  0.069  0.104  0.101   \n",
+       "\n",
+       "variable                 balanced_accuracy        roc_auc        num_feat  \\\n",
+       "statistics                            mean    std    mean    std     mean   \n",
+       "F3_marker_swe                        0.892  0.046   0.892  0.046      1.0   \n",
+       "F3_marker_te                         0.907  0.038   0.907  0.038      1.0   \n",
+       "F3_prot_Logistic                     0.871  0.044   0.960  0.019     21.0   \n",
+       "F3_marker_swe_Logistic               0.826  0.064   0.955  0.025      1.0   \n",
+       "F3_marker_elf                        0.820  0.049   0.820  0.049      1.0   \n",
+       "F3_marker_te_Logistic                0.798  0.050   0.955  0.019      1.0   \n",
+       "F3_marker_forns                      0.780  0.053   0.780  0.053      1.0   \n",
+       "F3_marker_ft                         0.762  0.069   0.762  0.069      1.0   \n",
+       "F3_marker_ft_Logistic                0.745  0.075   0.905  0.039      1.0   \n",
+       "F3_marker_fib4                       0.741  0.053   0.741  0.053      1.0   \n",
+       "F3_marker_forns_Logistic             0.715  0.059   0.842  0.055      1.0   \n",
+       "F3_marker_p3np_Logistic              0.690  0.059   0.840  0.049      1.0   \n",
+       "F3_marker_elf_Logistic               0.684  0.056   0.908  0.033      1.0   \n",
+       "F3_marker_apri                       0.622  0.052   0.622  0.052      1.0   \n",
+       "F3_marker_fib4_Logistic              0.596  0.046   0.836  0.052      1.0   \n",
+       "F3_marker_apri_Logistic              0.520  0.030   0.782  0.055      1.0   \n",
+       "\n",
+       "variable                       n_obs      roc_auc_2         \n",
+       "statistics                std   mean  std      mean    std  \n",
+       "F3_marker_swe             0.0  331.0  0.0     0.892  0.046  \n",
+       "F3_marker_te              0.0  341.0  0.0     0.907  0.038  \n",
+       "F3_prot_Logistic          0.0  358.0  0.0     0.960  0.019  \n",
+       "F3_marker_swe_Logistic    0.0  331.0  0.0     0.955  0.025  \n",
+       "F3_marker_elf             0.0  349.0  0.0     0.820  0.049  \n",
+       "F3_marker_te_Logistic     0.0  341.0  0.0     0.955  0.019  \n",
+       "F3_marker_forns           0.0  356.0  0.0     0.780  0.053  \n",
+       "F3_marker_ft              0.0  268.0  0.0     0.762  0.069  \n",
+       "F3_marker_ft_Logistic     0.0  268.0  0.0     0.905  0.039  \n",
+       "F3_marker_fib4            0.0  352.0  0.0     0.741  0.053  \n",
+       "F3_marker_forns_Logistic  0.0  356.0  0.0     0.842  0.055  \n",
+       "F3_marker_p3np_Logistic   0.0  319.0  0.0     0.840  0.049  \n",
+       "F3_marker_elf_Logistic    0.0  349.0  0.0     0.908  0.033  \n",
+       "F3_marker_apri            0.0  353.0  0.0     0.622  0.052  \n",
+       "F3_marker_fib4_Logistic   0.0  352.0  0.0     0.836  0.052  \n",
+       "F3_marker_apri_Logistic   0.0  353.0  0.0     0.782  0.055  "
+      ]
+     },
+     "execution_count": 261,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#proteins_selected = mrmr_markers['F3']\n",
     "proteins_selected = proteins_selected_f3\n",
@@ -2728,22 +10464,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 262,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    189\n",
+       "0    163\n",
+       "Name: nas_inflam, dtype: int64"
+      ]
+     },
+     "execution_count": 262,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "inflamation_ge_2.value_counts(dropna=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 263,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'aar': 2.0}"
+      ]
+     },
+     "execution_count": 263,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cutoffs_i2 = cutoffs_clinic['I2'].dropna().to_dict()\n",
     "cutoffs_i2"
@@ -2751,11 +10511,258 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 264,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>variable</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">precision</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">recall</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">f1</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">balanced_accuracy</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">roc_auc</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">num_feat</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">n_obs</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">roc_auc_2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>statistics</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>I2_prot_Logistic</th>\n",
+       "      <td>0.779</td>\n",
+       "      <td>0.059</td>\n",
+       "      <td>0.763</td>\n",
+       "      <td>0.078</td>\n",
+       "      <td>0.768</td>\n",
+       "      <td>0.052</td>\n",
+       "      <td>0.757</td>\n",
+       "      <td>0.045</td>\n",
+       "      <td>0.830</td>\n",
+       "      <td>0.044</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.830</td>\n",
+       "      <td>0.044</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I2_marker_ast_Logistic</th>\n",
+       "      <td>0.755</td>\n",
+       "      <td>0.074</td>\n",
+       "      <td>0.670</td>\n",
+       "      <td>0.106</td>\n",
+       "      <td>0.704</td>\n",
+       "      <td>0.072</td>\n",
+       "      <td>0.709</td>\n",
+       "      <td>0.058</td>\n",
+       "      <td>0.751</td>\n",
+       "      <td>0.062</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.751</td>\n",
+       "      <td>0.062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I2_marker_alt_Logistic</th>\n",
+       "      <td>0.596</td>\n",
+       "      <td>0.086</td>\n",
+       "      <td>0.789</td>\n",
+       "      <td>0.142</td>\n",
+       "      <td>0.665</td>\n",
+       "      <td>0.054</td>\n",
+       "      <td>0.576</td>\n",
+       "      <td>0.060</td>\n",
+       "      <td>0.655</td>\n",
+       "      <td>0.072</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>352.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.655</td>\n",
+       "      <td>0.072</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I2_marker_m30_Logistic</th>\n",
+       "      <td>0.753</td>\n",
+       "      <td>0.081</td>\n",
+       "      <td>0.594</td>\n",
+       "      <td>0.113</td>\n",
+       "      <td>0.657</td>\n",
+       "      <td>0.083</td>\n",
+       "      <td>0.692</td>\n",
+       "      <td>0.062</td>\n",
+       "      <td>0.768</td>\n",
+       "      <td>0.058</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>266.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.768</td>\n",
+       "      <td>0.058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I2_marker_m65_Logistic</th>\n",
+       "      <td>0.716</td>\n",
+       "      <td>0.086</td>\n",
+       "      <td>0.564</td>\n",
+       "      <td>0.098</td>\n",
+       "      <td>0.626</td>\n",
+       "      <td>0.083</td>\n",
+       "      <td>0.664</td>\n",
+       "      <td>0.063</td>\n",
+       "      <td>0.712</td>\n",
+       "      <td>0.071</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>264.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.712</td>\n",
+       "      <td>0.071</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I2_marker_aar_Logistic</th>\n",
+       "      <td>0.583</td>\n",
+       "      <td>0.085</td>\n",
+       "      <td>0.641</td>\n",
+       "      <td>0.109</td>\n",
+       "      <td>0.601</td>\n",
+       "      <td>0.058</td>\n",
+       "      <td>0.552</td>\n",
+       "      <td>0.054</td>\n",
+       "      <td>0.595</td>\n",
+       "      <td>0.065</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>344.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.595</td>\n",
+       "      <td>0.065</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I2_marker_m30m65_ratio_Logistic</th>\n",
+       "      <td>0.542</td>\n",
+       "      <td>0.109</td>\n",
+       "      <td>0.707</td>\n",
+       "      <td>0.252</td>\n",
+       "      <td>0.570</td>\n",
+       "      <td>0.092</td>\n",
+       "      <td>0.520</td>\n",
+       "      <td>0.047</td>\n",
+       "      <td>0.556</td>\n",
+       "      <td>0.068</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>264.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.556</td>\n",
+       "      <td>0.068</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I2_marker_aar</th>\n",
+       "      <td>0.809</td>\n",
+       "      <td>0.108</td>\n",
+       "      <td>0.211</td>\n",
+       "      <td>0.069</td>\n",
+       "      <td>0.331</td>\n",
+       "      <td>0.088</td>\n",
+       "      <td>0.577</td>\n",
+       "      <td>0.036</td>\n",
+       "      <td>0.577</td>\n",
+       "      <td>0.036</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>344.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.577</td>\n",
+       "      <td>0.036</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "variable                        precision        recall            f1         \\\n",
+       "statistics                           mean    std   mean    std   mean    std   \n",
+       "I2_prot_Logistic                    0.779  0.059  0.763  0.078  0.768  0.052   \n",
+       "I2_marker_ast_Logistic              0.755  0.074  0.670  0.106  0.704  0.072   \n",
+       "I2_marker_alt_Logistic              0.596  0.086  0.789  0.142  0.665  0.054   \n",
+       "I2_marker_m30_Logistic              0.753  0.081  0.594  0.113  0.657  0.083   \n",
+       "I2_marker_m65_Logistic              0.716  0.086  0.564  0.098  0.626  0.083   \n",
+       "I2_marker_aar_Logistic              0.583  0.085  0.641  0.109  0.601  0.058   \n",
+       "I2_marker_m30m65_ratio_Logistic     0.542  0.109  0.707  0.252  0.570  0.092   \n",
+       "I2_marker_aar                       0.809  0.108  0.211  0.069  0.331  0.088   \n",
+       "\n",
+       "variable                        balanced_accuracy        roc_auc         \\\n",
+       "statistics                                   mean    std    mean    std   \n",
+       "I2_prot_Logistic                            0.757  0.045   0.830  0.044   \n",
+       "I2_marker_ast_Logistic                      0.709  0.058   0.751  0.062   \n",
+       "I2_marker_alt_Logistic                      0.576  0.060   0.655  0.072   \n",
+       "I2_marker_m30_Logistic                      0.692  0.062   0.768  0.058   \n",
+       "I2_marker_m65_Logistic                      0.664  0.063   0.712  0.071   \n",
+       "I2_marker_aar_Logistic                      0.552  0.054   0.595  0.065   \n",
+       "I2_marker_m30m65_ratio_Logistic             0.520  0.047   0.556  0.068   \n",
+       "I2_marker_aar                               0.577  0.036   0.577  0.036   \n",
+       "\n",
+       "variable                        num_feat       n_obs      roc_auc_2         \n",
+       "statistics                          mean  std   mean  std      mean    std  \n",
+       "I2_prot_Logistic                     9.0  0.0  350.0  0.0     0.830  0.044  \n",
+       "I2_marker_ast_Logistic               1.0  0.0  345.0  0.0     0.751  0.062  \n",
+       "I2_marker_alt_Logistic               1.0  0.0  352.0  0.0     0.655  0.072  \n",
+       "I2_marker_m30_Logistic               1.0  0.0  266.0  0.0     0.768  0.058  \n",
+       "I2_marker_m65_Logistic               1.0  0.0  264.0  0.0     0.712  0.071  \n",
+       "I2_marker_aar_Logistic               1.0  0.0  344.0  0.0     0.595  0.065  \n",
+       "I2_marker_m30m65_ratio_Logistic      1.0  0.0  264.0  0.0     0.556  0.068  \n",
+       "I2_marker_aar                        1.0  0.0  344.0  0.0     0.577  0.036  "
+      ]
+     },
+     "execution_count": 264,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#proteins_selected = mrmr_markers['I2']\n",
     "proteins_selected = proteins_selected_I2\n",
@@ -2781,22 +10788,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 265,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    196\n",
+       "0    156\n",
+       "Name: nas_steatosis_ordinal, dtype: int64"
+      ]
+     },
+     "execution_count": 265,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "steatosis_ge_1.value_counts(dropna=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 266,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'cap': 290.0}"
+      ]
+     },
+     "execution_count": 266,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cutoffs_s1 = cutoffs_clinic['S1'].dropna().to_dict()\n",
     "cutoffs_s1"
@@ -2804,11 +10835,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 267,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    352.000\n",
+       "mean       0.557\n",
+       "std        0.497\n",
+       "min        0.000\n",
+       "25%        0.000\n",
+       "50%        1.000\n",
+       "75%        1.000\n",
+       "max        1.000\n",
+       "Name: nas_steatosis_ordinal, dtype: float64"
+      ]
+     },
+     "execution_count": 267,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "y = steatosis_ge_1.astype(int)\n",
     "y.describe()"
@@ -2816,11 +10866,148 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 268,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>variable</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">precision</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">recall</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">f1</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">balanced_accuracy</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">roc_auc</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">num_feat</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">n_obs</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">roc_auc_2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>statistics</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>S1_prot_Logistic</th>\n",
+       "      <td>0.823</td>\n",
+       "      <td>0.055</td>\n",
+       "      <td>0.842</td>\n",
+       "      <td>0.061</td>\n",
+       "      <td>0.831</td>\n",
+       "      <td>0.045</td>\n",
+       "      <td>0.807</td>\n",
+       "      <td>0.047</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.040</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>S1_marker_cap_Logistic</th>\n",
+       "      <td>0.749</td>\n",
+       "      <td>0.078</td>\n",
+       "      <td>0.933</td>\n",
+       "      <td>0.044</td>\n",
+       "      <td>0.828</td>\n",
+       "      <td>0.053</td>\n",
+       "      <td>0.689</td>\n",
+       "      <td>0.052</td>\n",
+       "      <td>0.824</td>\n",
+       "      <td>0.055</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>199.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.824</td>\n",
+       "      <td>0.055</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>S1_marker_cap</th>\n",
+       "      <td>0.890</td>\n",
+       "      <td>0.067</td>\n",
+       "      <td>0.632</td>\n",
+       "      <td>0.064</td>\n",
+       "      <td>0.737</td>\n",
+       "      <td>0.051</td>\n",
+       "      <td>0.747</td>\n",
+       "      <td>0.053</td>\n",
+       "      <td>0.747</td>\n",
+       "      <td>0.053</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>199.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.747</td>\n",
+       "      <td>0.053</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "variable               precision        recall            f1         \\\n",
+       "statistics                  mean    std   mean    std   mean    std   \n",
+       "S1_prot_Logistic           0.823  0.055  0.842  0.061  0.831  0.045   \n",
+       "S1_marker_cap_Logistic     0.749  0.078  0.933  0.044  0.828  0.053   \n",
+       "S1_marker_cap              0.890  0.067  0.632  0.064  0.737  0.051   \n",
+       "\n",
+       "variable               balanced_accuracy        roc_auc        num_feat       \\\n",
+       "statistics                          mean    std    mean    std     mean  std   \n",
+       "S1_prot_Logistic                   0.807  0.047   0.893  0.040     28.0  0.0   \n",
+       "S1_marker_cap_Logistic             0.689  0.052   0.824  0.055      1.0  0.0   \n",
+       "S1_marker_cap                      0.747  0.053   0.747  0.053      1.0  0.0   \n",
+       "\n",
+       "variable                n_obs      roc_auc_2         \n",
+       "statistics               mean  std      mean    std  \n",
+       "S1_prot_Logistic        350.0  0.0     0.893  0.040  \n",
+       "S1_marker_cap_Logistic  199.0  0.0     0.824  0.055  \n",
+       "S1_marker_cap           199.0  0.0     0.747  0.053  "
+      ]
+     },
+     "execution_count": 268,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#proteins_selected = mrmr_markers['S1']\n",
     "proteins_selected = proteins_selected_s1\n",
@@ -2846,7 +11033,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 269,
    "metadata": {
     "Collapsed": "false"
    },
@@ -2874,9 +11061,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 270,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'te': 'TE',\n",
+       " 'swe': 'SWE',\n",
+       " 'forns': 'Forns',\n",
+       " 'apri': 'APRI',\n",
+       " 'aar': 'AAR',\n",
+       " 'fib4': 'FIB-4',\n",
+       " 'elf': 'ELF',\n",
+       " 'p3np': 'P3NP',\n",
+       " 'ft': 'FibroTest',\n",
+       " 'm30': 'M30',\n",
+       " 'm65': 'M65',\n",
+       " 'cap': 'CAP'}"
+      ]
+     },
+     "execution_count": 270,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "map_names = pd.read_csv(os.path.join(FOLDER_DATA_RAW, 'naming_scheme.csv'), index_col='name_in_clinical_data')\n",
     "map_names = map_names['name_in_plot'].to_dict()\n",
@@ -2892,11 +11101,157 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 271,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>variable</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">precision</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">recall</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">f1</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">balanced_accuracy</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">roc_auc</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">num_feat</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">n_obs</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">roc_auc_2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>statistics</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>S1_prot_Logistic</th>\n",
+       "      <td>0.823</td>\n",
+       "      <td>0.055</td>\n",
+       "      <td>0.842</td>\n",
+       "      <td>0.061</td>\n",
+       "      <td>0.831</td>\n",
+       "      <td>0.045</td>\n",
+       "      <td>0.807</td>\n",
+       "      <td>0.047</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.040</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>S1_marker_cap_Logistic</th>\n",
+       "      <td>0.749</td>\n",
+       "      <td>0.078</td>\n",
+       "      <td>0.933</td>\n",
+       "      <td>0.044</td>\n",
+       "      <td>0.828</td>\n",
+       "      <td>0.053</td>\n",
+       "      <td>0.689</td>\n",
+       "      <td>0.052</td>\n",
+       "      <td>0.824</td>\n",
+       "      <td>0.055</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>199.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.824</td>\n",
+       "      <td>0.055</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>S1_marker_cap</th>\n",
+       "      <td>0.890</td>\n",
+       "      <td>0.067</td>\n",
+       "      <td>0.632</td>\n",
+       "      <td>0.064</td>\n",
+       "      <td>0.737</td>\n",
+       "      <td>0.051</td>\n",
+       "      <td>0.747</td>\n",
+       "      <td>0.053</td>\n",
+       "      <td>0.747</td>\n",
+       "      <td>0.053</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>199.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.747</td>\n",
+       "      <td>0.053</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "variable               precision        recall            f1         \\\n",
+       "statistics                  mean    std   mean    std   mean    std   \n",
+       "S1_prot_Logistic           0.823  0.055  0.842  0.061  0.831  0.045   \n",
+       "S1_marker_cap_Logistic     0.749  0.078  0.933  0.044  0.828  0.053   \n",
+       "S1_marker_cap              0.890  0.067  0.632  0.064  0.737  0.051   \n",
+       "\n",
+       "variable               balanced_accuracy        roc_auc        num_feat       \\\n",
+       "statistics                          mean    std    mean    std     mean  std   \n",
+       "S1_prot_Logistic                   0.807  0.047   0.893  0.040     28.0  0.0   \n",
+       "S1_marker_cap_Logistic             0.689  0.052   0.824  0.055      1.0  0.0   \n",
+       "S1_marker_cap                      0.747  0.053   0.747  0.053      1.0  0.0   \n",
+       "\n",
+       "variable                n_obs      roc_auc_2         \n",
+       "statistics               mean  std      mean    std  \n",
+       "S1_prot_Logistic        350.0  0.0     0.893  0.040  \n",
+       "S1_marker_cap_Logistic  199.0  0.0     0.824  0.055  \n",
+       "S1_marker_cap           199.0  0.0     0.747  0.053  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['Proteomics Model', 'CAP Model', 'CAP Test']"
+      ]
+     },
+     "execution_count": 271,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "display(result_table_s1)\n",
     "\n",
@@ -2946,11 +11301,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 272,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:title={'center':'Steatosis\\nBalanced accuracy'}>"
+      ]
+     },
+     "execution_count": 272,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from src.plots import plot_performance\n",
     "fig, ax = plt.subplots(figsize=(10,10))\n",
@@ -2968,11 +11346,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 273,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:title={'center':'TARGET\\nProteomic panel'}, xlabel='False Positive Rate', ylabel='True Positive Rate'>"
+      ]
+     },
+     "execution_count": 273,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from src.plots import plot_roc_curve\n",
     "\n",
@@ -2990,9 +11391,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 274,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from src.plots import plot_prc_curve\n",
     "fig, ax = plt.subplots(figsize=(8, 8))\n",
@@ -3010,20 +11424,456 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 275,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>variable</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">precision</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">recall</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">f1</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">balanced_accuracy</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">roc_auc</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">num_feat</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">n_obs</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">roc_auc_2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>statistics</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_swe</th>\n",
+       "      <td>0.857</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>0.833</td>\n",
+       "      <td>0.093</td>\n",
+       "      <td>0.840</td>\n",
+       "      <td>0.058</td>\n",
+       "      <td>0.892</td>\n",
+       "      <td>0.046</td>\n",
+       "      <td>0.892</td>\n",
+       "      <td>0.046</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>331.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.892</td>\n",
+       "      <td>0.046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_te</th>\n",
+       "      <td>0.792</td>\n",
+       "      <td>0.080</td>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.072</td>\n",
+       "      <td>0.836</td>\n",
+       "      <td>0.058</td>\n",
+       "      <td>0.907</td>\n",
+       "      <td>0.038</td>\n",
+       "      <td>0.907</td>\n",
+       "      <td>0.038</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>341.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.907</td>\n",
+       "      <td>0.038</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_prot_Logistic</th>\n",
+       "      <td>0.873</td>\n",
+       "      <td>0.066</td>\n",
+       "      <td>0.783</td>\n",
+       "      <td>0.084</td>\n",
+       "      <td>0.822</td>\n",
+       "      <td>0.059</td>\n",
+       "      <td>0.871</td>\n",
+       "      <td>0.044</td>\n",
+       "      <td>0.960</td>\n",
+       "      <td>0.019</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>358.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.960</td>\n",
+       "      <td>0.019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_swe_Logistic</th>\n",
+       "      <td>0.869</td>\n",
+       "      <td>0.080</td>\n",
+       "      <td>0.689</td>\n",
+       "      <td>0.134</td>\n",
+       "      <td>0.758</td>\n",
+       "      <td>0.087</td>\n",
+       "      <td>0.826</td>\n",
+       "      <td>0.064</td>\n",
+       "      <td>0.955</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>331.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.955</td>\n",
+       "      <td>0.025</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_elf</th>\n",
+       "      <td>0.700</td>\n",
+       "      <td>0.088</td>\n",
+       "      <td>0.752</td>\n",
+       "      <td>0.092</td>\n",
+       "      <td>0.722</td>\n",
+       "      <td>0.073</td>\n",
+       "      <td>0.820</td>\n",
+       "      <td>0.049</td>\n",
+       "      <td>0.820</td>\n",
+       "      <td>0.049</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>349.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.820</td>\n",
+       "      <td>0.049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_te_Logistic</th>\n",
+       "      <td>0.845</td>\n",
+       "      <td>0.088</td>\n",
+       "      <td>0.635</td>\n",
+       "      <td>0.105</td>\n",
+       "      <td>0.717</td>\n",
+       "      <td>0.074</td>\n",
+       "      <td>0.798</td>\n",
+       "      <td>0.050</td>\n",
+       "      <td>0.955</td>\n",
+       "      <td>0.019</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>341.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.955</td>\n",
+       "      <td>0.019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_forns</th>\n",
+       "      <td>0.641</td>\n",
+       "      <td>0.082</td>\n",
+       "      <td>0.703</td>\n",
+       "      <td>0.097</td>\n",
+       "      <td>0.667</td>\n",
+       "      <td>0.077</td>\n",
+       "      <td>0.780</td>\n",
+       "      <td>0.053</td>\n",
+       "      <td>0.780</td>\n",
+       "      <td>0.053</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>356.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.780</td>\n",
+       "      <td>0.053</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_ft</th>\n",
+       "      <td>0.645</td>\n",
+       "      <td>0.113</td>\n",
+       "      <td>0.638</td>\n",
+       "      <td>0.126</td>\n",
+       "      <td>0.635</td>\n",
+       "      <td>0.102</td>\n",
+       "      <td>0.762</td>\n",
+       "      <td>0.069</td>\n",
+       "      <td>0.762</td>\n",
+       "      <td>0.069</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>268.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.762</td>\n",
+       "      <td>0.069</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_ft_Logistic</th>\n",
+       "      <td>0.745</td>\n",
+       "      <td>0.112</td>\n",
+       "      <td>0.551</td>\n",
+       "      <td>0.149</td>\n",
+       "      <td>0.622</td>\n",
+       "      <td>0.123</td>\n",
+       "      <td>0.745</td>\n",
+       "      <td>0.075</td>\n",
+       "      <td>0.905</td>\n",
+       "      <td>0.039</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>268.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.905</td>\n",
+       "      <td>0.039</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_fib4</th>\n",
+       "      <td>0.688</td>\n",
+       "      <td>0.098</td>\n",
+       "      <td>0.574</td>\n",
+       "      <td>0.104</td>\n",
+       "      <td>0.620</td>\n",
+       "      <td>0.087</td>\n",
+       "      <td>0.741</td>\n",
+       "      <td>0.053</td>\n",
+       "      <td>0.741</td>\n",
+       "      <td>0.053</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>352.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.741</td>\n",
+       "      <td>0.053</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_forns_Logistic</th>\n",
+       "      <td>0.766</td>\n",
+       "      <td>0.121</td>\n",
+       "      <td>0.485</td>\n",
+       "      <td>0.116</td>\n",
+       "      <td>0.585</td>\n",
+       "      <td>0.105</td>\n",
+       "      <td>0.715</td>\n",
+       "      <td>0.059</td>\n",
+       "      <td>0.842</td>\n",
+       "      <td>0.055</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>356.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.842</td>\n",
+       "      <td>0.055</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_p3np_Logistic</th>\n",
+       "      <td>0.745</td>\n",
+       "      <td>0.129</td>\n",
+       "      <td>0.425</td>\n",
+       "      <td>0.120</td>\n",
+       "      <td>0.531</td>\n",
+       "      <td>0.112</td>\n",
+       "      <td>0.690</td>\n",
+       "      <td>0.059</td>\n",
+       "      <td>0.840</td>\n",
+       "      <td>0.049</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>319.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.840</td>\n",
+       "      <td>0.049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_elf_Logistic</th>\n",
+       "      <td>0.914</td>\n",
+       "      <td>0.097</td>\n",
+       "      <td>0.380</td>\n",
+       "      <td>0.116</td>\n",
+       "      <td>0.524</td>\n",
+       "      <td>0.117</td>\n",
+       "      <td>0.684</td>\n",
+       "      <td>0.056</td>\n",
+       "      <td>0.908</td>\n",
+       "      <td>0.033</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>349.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.908</td>\n",
+       "      <td>0.033</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_apri</th>\n",
+       "      <td>0.512</td>\n",
+       "      <td>0.114</td>\n",
+       "      <td>0.367</td>\n",
+       "      <td>0.104</td>\n",
+       "      <td>0.421</td>\n",
+       "      <td>0.098</td>\n",
+       "      <td>0.622</td>\n",
+       "      <td>0.052</td>\n",
+       "      <td>0.622</td>\n",
+       "      <td>0.052</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.622</td>\n",
+       "      <td>0.052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_fib4_Logistic</th>\n",
+       "      <td>0.706</td>\n",
+       "      <td>0.169</td>\n",
+       "      <td>0.232</td>\n",
+       "      <td>0.103</td>\n",
+       "      <td>0.334</td>\n",
+       "      <td>0.111</td>\n",
+       "      <td>0.596</td>\n",
+       "      <td>0.046</td>\n",
+       "      <td>0.836</td>\n",
+       "      <td>0.052</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>352.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.836</td>\n",
+       "      <td>0.052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F3_marker_apri_Logistic</th>\n",
+       "      <td>0.391</td>\n",
+       "      <td>0.354</td>\n",
+       "      <td>0.065</td>\n",
+       "      <td>0.069</td>\n",
+       "      <td>0.104</td>\n",
+       "      <td>0.101</td>\n",
+       "      <td>0.520</td>\n",
+       "      <td>0.030</td>\n",
+       "      <td>0.782</td>\n",
+       "      <td>0.055</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.782</td>\n",
+       "      <td>0.055</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "variable                 precision        recall            f1         \\\n",
+       "statistics                    mean    std   mean    std   mean    std   \n",
+       "F3_marker_swe                0.857  0.076  0.833  0.093  0.840  0.058   \n",
+       "F3_marker_te                 0.792  0.080  0.893  0.072  0.836  0.058   \n",
+       "F3_prot_Logistic             0.873  0.066  0.783  0.084  0.822  0.059   \n",
+       "F3_marker_swe_Logistic       0.869  0.080  0.689  0.134  0.758  0.087   \n",
+       "F3_marker_elf                0.700  0.088  0.752  0.092  0.722  0.073   \n",
+       "F3_marker_te_Logistic        0.845  0.088  0.635  0.105  0.717  0.074   \n",
+       "F3_marker_forns              0.641  0.082  0.703  0.097  0.667  0.077   \n",
+       "F3_marker_ft                 0.645  0.113  0.638  0.126  0.635  0.102   \n",
+       "F3_marker_ft_Logistic        0.745  0.112  0.551  0.149  0.622  0.123   \n",
+       "F3_marker_fib4               0.688  0.098  0.574  0.104  0.620  0.087   \n",
+       "F3_marker_forns_Logistic     0.766  0.121  0.485  0.116  0.585  0.105   \n",
+       "F3_marker_p3np_Logistic      0.745  0.129  0.425  0.120  0.531  0.112   \n",
+       "F3_marker_elf_Logistic       0.914  0.097  0.380  0.116  0.524  0.117   \n",
+       "F3_marker_apri               0.512  0.114  0.367  0.104  0.421  0.098   \n",
+       "F3_marker_fib4_Logistic      0.706  0.169  0.232  0.103  0.334  0.111   \n",
+       "F3_marker_apri_Logistic      0.391  0.354  0.065  0.069  0.104  0.101   \n",
+       "\n",
+       "variable                 balanced_accuracy        roc_auc        num_feat  \\\n",
+       "statistics                            mean    std    mean    std     mean   \n",
+       "F3_marker_swe                        0.892  0.046   0.892  0.046      1.0   \n",
+       "F3_marker_te                         0.907  0.038   0.907  0.038      1.0   \n",
+       "F3_prot_Logistic                     0.871  0.044   0.960  0.019     21.0   \n",
+       "F3_marker_swe_Logistic               0.826  0.064   0.955  0.025      1.0   \n",
+       "F3_marker_elf                        0.820  0.049   0.820  0.049      1.0   \n",
+       "F3_marker_te_Logistic                0.798  0.050   0.955  0.019      1.0   \n",
+       "F3_marker_forns                      0.780  0.053   0.780  0.053      1.0   \n",
+       "F3_marker_ft                         0.762  0.069   0.762  0.069      1.0   \n",
+       "F3_marker_ft_Logistic                0.745  0.075   0.905  0.039      1.0   \n",
+       "F3_marker_fib4                       0.741  0.053   0.741  0.053      1.0   \n",
+       "F3_marker_forns_Logistic             0.715  0.059   0.842  0.055      1.0   \n",
+       "F3_marker_p3np_Logistic              0.690  0.059   0.840  0.049      1.0   \n",
+       "F3_marker_elf_Logistic               0.684  0.056   0.908  0.033      1.0   \n",
+       "F3_marker_apri                       0.622  0.052   0.622  0.052      1.0   \n",
+       "F3_marker_fib4_Logistic              0.596  0.046   0.836  0.052      1.0   \n",
+       "F3_marker_apri_Logistic              0.520  0.030   0.782  0.055      1.0   \n",
+       "\n",
+       "variable                       n_obs      roc_auc_2         \n",
+       "statistics                std   mean  std      mean    std  \n",
+       "F3_marker_swe             0.0  331.0  0.0     0.892  0.046  \n",
+       "F3_marker_te              0.0  341.0  0.0     0.907  0.038  \n",
+       "F3_prot_Logistic          0.0  358.0  0.0     0.960  0.019  \n",
+       "F3_marker_swe_Logistic    0.0  331.0  0.0     0.955  0.025  \n",
+       "F3_marker_elf             0.0  349.0  0.0     0.820  0.049  \n",
+       "F3_marker_te_Logistic     0.0  341.0  0.0     0.955  0.019  \n",
+       "F3_marker_forns           0.0  356.0  0.0     0.780  0.053  \n",
+       "F3_marker_ft              0.0  268.0  0.0     0.762  0.069  \n",
+       "F3_marker_ft_Logistic     0.0  268.0  0.0     0.905  0.039  \n",
+       "F3_marker_fib4            0.0  352.0  0.0     0.741  0.053  \n",
+       "F3_marker_forns_Logistic  0.0  356.0  0.0     0.842  0.055  \n",
+       "F3_marker_p3np_Logistic   0.0  319.0  0.0     0.840  0.049  \n",
+       "F3_marker_elf_Logistic    0.0  349.0  0.0     0.908  0.033  \n",
+       "F3_marker_apri            0.0  353.0  0.0     0.622  0.052  \n",
+       "F3_marker_fib4_Logistic   0.0  352.0  0.0     0.836  0.052  \n",
+       "F3_marker_apri_Logistic   0.0  353.0  0.0     0.782  0.055  "
+      ]
+     },
+     "execution_count": 275,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "result_table_f3"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 276,
    "metadata": {
     "Collapsed": "false"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import string\n",
     "fig, axs = plt.subplots(3,3,figsize=(20,20))\n",
@@ -3105,7 +11955,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 277,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3119,9 +11969,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 278,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['F2_marker_te', 'F2_marker_swe', 'F2_marker_elf', 'F2_marker_ft', 'F2_marker_fib4', 'F2_marker_apri', 'F2_marker_forns_Logistic', 'F2_marker_p3np_Logistic', 'F2_marker_te_Logistic', 'F2_marker_swe_Logistic', 'F2_marker_elf_Logistic', 'F2_marker_ft_Logistic', 'F2_marker_fib4_Logistic', 'F2_marker_apri_Logistic', 'F2_prot_Logistic', 'F3_marker_te', 'F3_marker_swe', 'F3_marker_elf', 'F3_marker_ft', 'F3_marker_fib4', 'F3_marker_apri', 'F3_marker_forns', 'F3_marker_p3np_Logistic', 'F3_marker_te_Logistic', 'F3_marker_swe_Logistic', 'F3_marker_elf_Logistic', 'F3_marker_ft_Logistic', 'F3_marker_fib4_Logistic', 'F3_marker_apri_Logistic', 'F3_marker_forns_Logistic', 'F3_prot_Logistic', 'I2_marker_aar', 'I2_marker_m30_Logistic', 'I2_marker_m65_Logistic', 'I2_marker_alt_Logistic', 'I2_marker_ast_Logistic', 'I2_marker_m30m65_ratio_Logistic', 'I2_marker_aar_Logistic', 'I2_prot_Logistic', 'S1_marker_cap', 'S1_marker_cap_Logistic', 'S1_prot_Logistic'])"
+      ]
+     },
+     "execution_count": 278,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "results_combinded = {}\n",
     "for _results in results.values(): results_combinded.update(_results)\n",
@@ -3130,7 +11991,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 279,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3149,9 +12010,360 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 280,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>F3_marker_swe</th>\n",
+       "      <th>F3_marker_te</th>\n",
+       "      <th>S1_prot_Logistic</th>\n",
+       "      <th>S1_marker_cap_Logistic</th>\n",
+       "      <th>F3_prot_Logistic</th>\n",
+       "      <th>F2_prot_Logistic</th>\n",
+       "      <th>F2_marker_elf_Logistic</th>\n",
+       "      <th>F2_marker_te</th>\n",
+       "      <th>F2_marker_swe</th>\n",
+       "      <th>F2_marker_te_Logistic</th>\n",
+       "      <th>...</th>\n",
+       "      <th>F3_marker_fib4</th>\n",
+       "      <th>I2_marker_aar_Logistic</th>\n",
+       "      <th>F3_marker_forns_Logistic</th>\n",
+       "      <th>I2_marker_m30m65_ratio_Logistic</th>\n",
+       "      <th>F3_marker_p3np_Logistic</th>\n",
+       "      <th>F3_marker_elf_Logistic</th>\n",
+       "      <th>F3_marker_apri</th>\n",
+       "      <th>F3_marker_fib4_Logistic</th>\n",
+       "      <th>I2_marker_aar</th>\n",
+       "      <th>F3_marker_apri_Logistic</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f1</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>50.000</td>\n",
+       "      <td>50.000</td>\n",
+       "      <td>50.000</td>\n",
+       "      <td>50.000</td>\n",
+       "      <td>50.000</td>\n",
+       "      <td>50.000</td>\n",
+       "      <td>50.000</td>\n",
+       "      <td>50.000</td>\n",
+       "      <td>50.000</td>\n",
+       "      <td>50.000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>50.000</td>\n",
+       "      <td>50.000</td>\n",
+       "      <td>50.000</td>\n",
+       "      <td>50.000</td>\n",
+       "      <td>50.000</td>\n",
+       "      <td>50.000</td>\n",
+       "      <td>50.000</td>\n",
+       "      <td>50.000</td>\n",
+       "      <td>50.000</td>\n",
+       "      <td>50.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.840</td>\n",
+       "      <td>0.836</td>\n",
+       "      <td>0.831</td>\n",
+       "      <td>0.828</td>\n",
+       "      <td>0.822</td>\n",
+       "      <td>0.820</td>\n",
+       "      <td>0.790</td>\n",
+       "      <td>0.788</td>\n",
+       "      <td>0.784</td>\n",
+       "      <td>0.783</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.620</td>\n",
+       "      <td>0.601</td>\n",
+       "      <td>0.585</td>\n",
+       "      <td>0.570</td>\n",
+       "      <td>0.531</td>\n",
+       "      <td>0.524</td>\n",
+       "      <td>0.421</td>\n",
+       "      <td>0.334</td>\n",
+       "      <td>0.331</td>\n",
+       "      <td>0.104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.059</td>\n",
+       "      <td>0.059</td>\n",
+       "      <td>0.045</td>\n",
+       "      <td>0.054</td>\n",
+       "      <td>0.059</td>\n",
+       "      <td>0.042</td>\n",
+       "      <td>0.045</td>\n",
+       "      <td>0.048</td>\n",
+       "      <td>0.041</td>\n",
+       "      <td>0.051</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.088</td>\n",
+       "      <td>0.059</td>\n",
+       "      <td>0.106</td>\n",
+       "      <td>0.093</td>\n",
+       "      <td>0.113</td>\n",
+       "      <td>0.118</td>\n",
+       "      <td>0.099</td>\n",
+       "      <td>0.112</td>\n",
+       "      <td>0.089</td>\n",
+       "      <td>0.103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.710</td>\n",
+       "      <td>0.722</td>\n",
+       "      <td>0.722</td>\n",
+       "      <td>0.683</td>\n",
+       "      <td>0.684</td>\n",
+       "      <td>0.694</td>\n",
+       "      <td>0.685</td>\n",
+       "      <td>0.677</td>\n",
+       "      <td>0.700</td>\n",
+       "      <td>0.656</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.370</td>\n",
+       "      <td>0.478</td>\n",
+       "      <td>0.348</td>\n",
+       "      <td>0.341</td>\n",
+       "      <td>0.222</td>\n",
+       "      <td>0.095</td>\n",
+       "      <td>0.154</td>\n",
+       "      <td>0.160</td>\n",
+       "      <td>0.130</td>\n",
+       "      <td>0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.812</td>\n",
+       "      <td>0.788</td>\n",
+       "      <td>0.801</td>\n",
+       "      <td>0.800</td>\n",
+       "      <td>0.783</td>\n",
+       "      <td>0.800</td>\n",
+       "      <td>0.763</td>\n",
+       "      <td>0.761</td>\n",
+       "      <td>0.758</td>\n",
+       "      <td>0.755</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.565</td>\n",
+       "      <td>0.568</td>\n",
+       "      <td>0.500</td>\n",
+       "      <td>0.518</td>\n",
+       "      <td>0.472</td>\n",
+       "      <td>0.464</td>\n",
+       "      <td>0.360</td>\n",
+       "      <td>0.250</td>\n",
+       "      <td>0.274</td>\n",
+       "      <td>0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.847</td>\n",
+       "      <td>0.841</td>\n",
+       "      <td>0.833</td>\n",
+       "      <td>0.832</td>\n",
+       "      <td>0.829</td>\n",
+       "      <td>0.825</td>\n",
+       "      <td>0.800</td>\n",
+       "      <td>0.785</td>\n",
+       "      <td>0.779</td>\n",
+       "      <td>0.780</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.623</td>\n",
+       "      <td>0.590</td>\n",
+       "      <td>0.606</td>\n",
+       "      <td>0.597</td>\n",
+       "      <td>0.542</td>\n",
+       "      <td>0.538</td>\n",
+       "      <td>0.412</td>\n",
+       "      <td>0.316</td>\n",
+       "      <td>0.326</td>\n",
+       "      <td>0.093</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.882</td>\n",
+       "      <td>0.880</td>\n",
+       "      <td>0.867</td>\n",
+       "      <td>0.867</td>\n",
+       "      <td>0.857</td>\n",
+       "      <td>0.843</td>\n",
+       "      <td>0.810</td>\n",
+       "      <td>0.817</td>\n",
+       "      <td>0.811</td>\n",
+       "      <td>0.824</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.686</td>\n",
+       "      <td>0.640</td>\n",
+       "      <td>0.686</td>\n",
+       "      <td>0.641</td>\n",
+       "      <td>0.600</td>\n",
+       "      <td>0.580</td>\n",
+       "      <td>0.497</td>\n",
+       "      <td>0.423</td>\n",
+       "      <td>0.398</td>\n",
+       "      <td>0.170</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.941</td>\n",
+       "      <td>0.950</td>\n",
+       "      <td>0.909</td>\n",
+       "      <td>0.939</td>\n",
+       "      <td>0.933</td>\n",
+       "      <td>0.891</td>\n",
+       "      <td>0.902</td>\n",
+       "      <td>0.909</td>\n",
+       "      <td>0.865</td>\n",
+       "      <td>0.875</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.833</td>\n",
+       "      <td>0.750</td>\n",
+       "      <td>0.732</td>\n",
+       "      <td>0.712</td>\n",
+       "      <td>0.759</td>\n",
+       "      <td>0.769</td>\n",
+       "      <td>0.651</td>\n",
+       "      <td>0.615</td>\n",
+       "      <td>0.556</td>\n",
+       "      <td>0.400</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 42 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       F3_marker_swe  F3_marker_te  S1_prot_Logistic  S1_marker_cap_Logistic  \\\n",
+       "f1                                                                             \n",
+       "count         50.000        50.000            50.000                  50.000   \n",
+       "mean           0.840         0.836             0.831                   0.828   \n",
+       "std            0.059         0.059             0.045                   0.054   \n",
+       "min            0.710         0.722             0.722                   0.683   \n",
+       "25%            0.812         0.788             0.801                   0.800   \n",
+       "50%            0.847         0.841             0.833                   0.832   \n",
+       "75%            0.882         0.880             0.867                   0.867   \n",
+       "max            0.941         0.950             0.909                   0.939   \n",
+       "\n",
+       "       F3_prot_Logistic  F2_prot_Logistic  F2_marker_elf_Logistic  \\\n",
+       "f1                                                                  \n",
+       "count            50.000            50.000                  50.000   \n",
+       "mean              0.822             0.820                   0.790   \n",
+       "std               0.059             0.042                   0.045   \n",
+       "min               0.684             0.694                   0.685   \n",
+       "25%               0.783             0.800                   0.763   \n",
+       "50%               0.829             0.825                   0.800   \n",
+       "75%               0.857             0.843                   0.810   \n",
+       "max               0.933             0.891                   0.902   \n",
+       "\n",
+       "       F2_marker_te  F2_marker_swe  F2_marker_te_Logistic  ...  \\\n",
+       "f1                                                         ...   \n",
+       "count        50.000         50.000                 50.000  ...   \n",
+       "mean          0.788          0.784                  0.783  ...   \n",
+       "std           0.048          0.041                  0.051  ...   \n",
+       "min           0.677          0.700                  0.656  ...   \n",
+       "25%           0.761          0.758                  0.755  ...   \n",
+       "50%           0.785          0.779                  0.780  ...   \n",
+       "75%           0.817          0.811                  0.824  ...   \n",
+       "max           0.909          0.865                  0.875  ...   \n",
+       "\n",
+       "       F3_marker_fib4  I2_marker_aar_Logistic  F3_marker_forns_Logistic  \\\n",
+       "f1                                                                        \n",
+       "count          50.000                  50.000                    50.000   \n",
+       "mean            0.620                   0.601                     0.585   \n",
+       "std             0.088                   0.059                     0.106   \n",
+       "min             0.370                   0.478                     0.348   \n",
+       "25%             0.565                   0.568                     0.500   \n",
+       "50%             0.623                   0.590                     0.606   \n",
+       "75%             0.686                   0.640                     0.686   \n",
+       "max             0.833                   0.750                     0.732   \n",
+       "\n",
+       "       I2_marker_m30m65_ratio_Logistic  F3_marker_p3np_Logistic  \\\n",
+       "f1                                                                \n",
+       "count                           50.000                   50.000   \n",
+       "mean                             0.570                    0.531   \n",
+       "std                              0.093                    0.113   \n",
+       "min                              0.341                    0.222   \n",
+       "25%                              0.518                    0.472   \n",
+       "50%                              0.597                    0.542   \n",
+       "75%                              0.641                    0.600   \n",
+       "max                              0.712                    0.759   \n",
+       "\n",
+       "       F3_marker_elf_Logistic  F3_marker_apri  F3_marker_fib4_Logistic  \\\n",
+       "f1                                                                       \n",
+       "count                  50.000          50.000                   50.000   \n",
+       "mean                    0.524           0.421                    0.334   \n",
+       "std                     0.118           0.099                    0.112   \n",
+       "min                     0.095           0.154                    0.160   \n",
+       "25%                     0.464           0.360                    0.250   \n",
+       "50%                     0.538           0.412                    0.316   \n",
+       "75%                     0.580           0.497                    0.423   \n",
+       "max                     0.769           0.651                    0.615   \n",
+       "\n",
+       "       I2_marker_aar  F3_marker_apri_Logistic  \n",
+       "f1                                             \n",
+       "count         50.000                   50.000  \n",
+       "mean           0.331                    0.104  \n",
+       "std            0.089                    0.103  \n",
+       "min            0.130                    0.000  \n",
+       "25%            0.274                    0.000  \n",
+       "50%            0.326                    0.093  \n",
+       "75%            0.398                    0.170  \n",
+       "max            0.556                    0.400  \n",
+       "\n",
+       "[8 rows x 42 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "out= widgets.interact(show_summary, metric=scoring, results_dict=widgets.fixed(results_combinded))"
    ]
@@ -3165,7 +12377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 281,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3176,7 +12388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 282,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3208,7 +12420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 283,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3226,9 +12438,137 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 284,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>median</th>\n",
+       "      <th>F2_prot_Logistic</th>\n",
+       "      <th>F2_marker_ft</th>\n",
+       "      <th>F2_marker_ft_Logistic</th>\n",
+       "      <th>F2_marker_te_Logistic</th>\n",
+       "      <th>F2_marker_swe_Logistic</th>\n",
+       "      <th>F2_marker_elf_Logistic</th>\n",
+       "      <th>F2_marker_swe</th>\n",
+       "      <th>F2_marker_p3np_Logistic</th>\n",
+       "      <th>F2_marker_forns_Logistic</th>\n",
+       "      <th>F2_marker_apri_Logistic</th>\n",
+       "      <th>F2_marker_apri</th>\n",
+       "      <th>F2_marker_te</th>\n",
+       "      <th>F2_marker_fib4_Logistic</th>\n",
+       "      <th>F2_marker_fib4</th>\n",
+       "      <th>F2_marker_elf</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>run</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.86</td>\n",
+       "      <td>0.857</td>\n",
+       "      <td>0.818</td>\n",
+       "      <td>0.906</td>\n",
+       "      <td>0.867</td>\n",
+       "      <td>0.816</td>\n",
+       "      <td>0.806</td>\n",
+       "      <td>0.781</td>\n",
+       "      <td>0.778</td>\n",
+       "      <td>0.862</td>\n",
+       "      <td>0.840</td>\n",
+       "      <td>0.762</td>\n",
+       "      <td>0.833</td>\n",
+       "      <td>0.795</td>\n",
+       "      <td>0.591</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>0.86</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.900</td>\n",
+       "      <td>0.895</td>\n",
+       "      <td>0.882</td>\n",
+       "      <td>0.854</td>\n",
+       "      <td>0.825</td>\n",
+       "      <td>0.818</td>\n",
+       "      <td>0.783</td>\n",
+       "      <td>0.775</td>\n",
+       "      <td>0.771</td>\n",
+       "      <td>0.766</td>\n",
+       "      <td>0.762</td>\n",
+       "      <td>0.739</td>\n",
+       "      <td>0.657</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "median  F2_prot_Logistic  F2_marker_ft  F2_marker_ft_Logistic  \\\n",
+       "run                                                             \n",
+       "6                   0.86         0.857                  0.818   \n",
+       "38                  0.86         1.000                  0.900   \n",
+       "\n",
+       "median  F2_marker_te_Logistic  F2_marker_swe_Logistic  F2_marker_elf_Logistic  \\\n",
+       "run                                                                             \n",
+       "6                       0.906                   0.867                   0.816   \n",
+       "38                      0.895                   0.882                   0.854   \n",
+       "\n",
+       "median  F2_marker_swe  F2_marker_p3np_Logistic  F2_marker_forns_Logistic  \\\n",
+       "run                                                                        \n",
+       "6               0.806                    0.781                     0.778   \n",
+       "38              0.825                    0.818                     0.783   \n",
+       "\n",
+       "median  F2_marker_apri_Logistic  F2_marker_apri  F2_marker_te  \\\n",
+       "run                                                             \n",
+       "6                         0.862           0.840         0.762   \n",
+       "38                        0.775           0.771         0.766   \n",
+       "\n",
+       "median  F2_marker_fib4_Logistic  F2_marker_fib4  F2_marker_elf  \n",
+       "run                                                             \n",
+       "6                         0.833           0.795          0.591  \n",
+       "38                        0.762           0.739          0.657  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "out2= widgets.interact(_caller_comp, metric=scoring, endpoint=results.keys(), selector=metrics_np_fct.keys())"
    ]
@@ -3249,9 +12589,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 285,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:N train: 288\n",
+      "INFO:root:N test: 72\n"
+     ]
+    }
+   ],
    "source": [
     "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(data_cli_missing_table, stratifier, test_size=0.2, stratify=stratifier, random_state=42)\n",
     "logging.info(f\"N train: {len(y_train)}\")\n",
@@ -3260,16 +12609,115 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 286,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
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+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>train</th>\n",
+       "      <th>test</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>111111000011011000001</th>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111111111111011000001</th>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111111111111101111110</th>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111111111111110111110</th>\n",
+       "      <td>18</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111111111111111000001</th>\n",
+       "      <td>21</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111111111111111100000</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111111111111111100001</th>\n",
+       "      <td>28</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111111111111111101001</th>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111111111111111111110</th>\n",
+       "      <td>87</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111111111111111111111</th>\n",
+       "      <td>97</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       train  test\n",
+       "111111000011011000001      6     1\n",
+       "111111111111011000001      8     2\n",
+       "111111111111101111110     10     3\n",
+       "111111111111110111110     18     5\n",
+       "111111111111111000001     21     5\n",
+       "111111111111111100000      7     2\n",
+       "111111111111111100001     28     7\n",
+       "111111111111111101001      6     1\n",
+       "111111111111111111110     87    22\n",
+       "111111111111111111111     97    24"
+      ]
+     },
+     "execution_count": 286,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "combine_value_counts(pd.DataFrame({'train': y_train, 'test': y_test}))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 287,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3285,7 +12733,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 288,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3298,7 +12746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 289,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3330,9 +12778,250 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 290,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1</th>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <th>roc_auc</th>\n",
+       "      <th>num_feat</th>\n",
+       "      <th>n_obs</th>\n",
+       "      <th>roc_auc_2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>F2_prot_Logistic</th>\n",
+       "      <td>0.935</td>\n",
+       "      <td>0.763</td>\n",
+       "      <td>0.841</td>\n",
+       "      <td>0.852</td>\n",
+       "      <td>0.922</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>358.0</td>\n",
+       "      <td>0.922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_swe_Logistic</th>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.758</td>\n",
+       "      <td>0.820</td>\n",
+       "      <td>0.832</td>\n",
+       "      <td>0.895</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>331.0</td>\n",
+       "      <td>0.895</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_elf_Logistic</th>\n",
+       "      <td>0.733</td>\n",
+       "      <td>0.892</td>\n",
+       "      <td>0.805</td>\n",
+       "      <td>0.764</td>\n",
+       "      <td>0.865</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>349.0</td>\n",
+       "      <td>0.865</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_swe</th>\n",
+       "      <td>0.812</td>\n",
+       "      <td>0.788</td>\n",
+       "      <td>0.800</td>\n",
+       "      <td>0.800</td>\n",
+       "      <td>0.800</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>331.0</td>\n",
+       "      <td>0.800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_p3np_Logistic</th>\n",
+       "      <td>0.806</td>\n",
+       "      <td>0.781</td>\n",
+       "      <td>0.794</td>\n",
+       "      <td>0.797</td>\n",
+       "      <td>0.855</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>319.0</td>\n",
+       "      <td>0.855</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_te_Logistic</th>\n",
+       "      <td>0.833</td>\n",
+       "      <td>0.735</td>\n",
+       "      <td>0.781</td>\n",
+       "      <td>0.794</td>\n",
+       "      <td>0.868</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>341.0</td>\n",
+       "      <td>0.868</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_te</th>\n",
+       "      <td>0.750</td>\n",
+       "      <td>0.794</td>\n",
+       "      <td>0.771</td>\n",
+       "      <td>0.765</td>\n",
+       "      <td>0.765</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>341.0</td>\n",
+       "      <td>0.765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_forns_Logistic</th>\n",
+       "      <td>0.700</td>\n",
+       "      <td>0.737</td>\n",
+       "      <td>0.718</td>\n",
+       "      <td>0.692</td>\n",
+       "      <td>0.786</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>356.0</td>\n",
+       "      <td>0.786</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_apri_Logistic</th>\n",
+       "      <td>0.846</td>\n",
+       "      <td>0.595</td>\n",
+       "      <td>0.698</td>\n",
+       "      <td>0.738</td>\n",
+       "      <td>0.750</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>0.750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_elf</th>\n",
+       "      <td>0.536</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.698</td>\n",
+       "      <td>0.515</td>\n",
+       "      <td>0.515</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>349.0</td>\n",
+       "      <td>0.515</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_ft_Logistic</th>\n",
+       "      <td>0.773</td>\n",
+       "      <td>0.630</td>\n",
+       "      <td>0.694</td>\n",
+       "      <td>0.722</td>\n",
+       "      <td>0.820</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>268.0</td>\n",
+       "      <td>0.820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_fib4</th>\n",
+       "      <td>0.667</td>\n",
+       "      <td>0.649</td>\n",
+       "      <td>0.658</td>\n",
+       "      <td>0.648</td>\n",
+       "      <td>0.648</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>352.0</td>\n",
+       "      <td>0.648</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_fib4_Logistic</th>\n",
+       "      <td>0.688</td>\n",
+       "      <td>0.595</td>\n",
+       "      <td>0.638</td>\n",
+       "      <td>0.650</td>\n",
+       "      <td>0.715</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>352.0</td>\n",
+       "      <td>0.715</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_apri</th>\n",
+       "      <td>0.850</td>\n",
+       "      <td>0.459</td>\n",
+       "      <td>0.596</td>\n",
+       "      <td>0.686</td>\n",
+       "      <td>0.686</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>0.686</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F2_marker_ft</th>\n",
+       "      <td>0.917</td>\n",
+       "      <td>0.407</td>\n",
+       "      <td>0.564</td>\n",
+       "      <td>0.685</td>\n",
+       "      <td>0.685</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>268.0</td>\n",
+       "      <td>0.685</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          precision  recall     f1  balanced_accuracy  \\\n",
+       "F2_prot_Logistic              0.935   0.763  0.841              0.852   \n",
+       "F2_marker_swe_Logistic        0.893   0.758  0.820              0.832   \n",
+       "F2_marker_elf_Logistic        0.733   0.892  0.805              0.764   \n",
+       "F2_marker_swe                 0.812   0.788  0.800              0.800   \n",
+       "F2_marker_p3np_Logistic       0.806   0.781  0.794              0.797   \n",
+       "F2_marker_te_Logistic         0.833   0.735  0.781              0.794   \n",
+       "F2_marker_te                  0.750   0.794  0.771              0.765   \n",
+       "F2_marker_forns_Logistic      0.700   0.737  0.718              0.692   \n",
+       "F2_marker_apri_Logistic       0.846   0.595  0.698              0.738   \n",
+       "F2_marker_elf                 0.536   1.000  0.698              0.515   \n",
+       "F2_marker_ft_Logistic         0.773   0.630  0.694              0.722   \n",
+       "F2_marker_fib4                0.667   0.649  0.658              0.648   \n",
+       "F2_marker_fib4_Logistic       0.688   0.595  0.638              0.650   \n",
+       "F2_marker_apri                0.850   0.459  0.596              0.686   \n",
+       "F2_marker_ft                  0.917   0.407  0.564              0.685   \n",
+       "\n",
+       "                          roc_auc  num_feat  n_obs  roc_auc_2  \n",
+       "F2_prot_Logistic            0.922      14.0  358.0      0.922  \n",
+       "F2_marker_swe_Logistic      0.895       1.0  331.0      0.895  \n",
+       "F2_marker_elf_Logistic      0.865       1.0  349.0      0.865  \n",
+       "F2_marker_swe               0.800       1.0  331.0      0.800  \n",
+       "F2_marker_p3np_Logistic     0.855       1.0  319.0      0.855  \n",
+       "F2_marker_te_Logistic       0.868       1.0  341.0      0.868  \n",
+       "F2_marker_te                0.765       1.0  341.0      0.765  \n",
+       "F2_marker_forns_Logistic    0.786       1.0  356.0      0.786  \n",
+       "F2_marker_apri_Logistic     0.750       1.0  353.0      0.750  \n",
+       "F2_marker_elf               0.515       1.0  349.0      0.515  \n",
+       "F2_marker_ft_Logistic       0.820       1.0  268.0      0.820  \n",
+       "F2_marker_fib4              0.648       1.0  352.0      0.648  \n",
+       "F2_marker_fib4_Logistic     0.715       1.0  352.0      0.715  \n",
+       "F2_marker_apri              0.686       1.0  353.0      0.686  \n",
+       "F2_marker_ft                0.685       1.0  268.0      0.685  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "def display_result(endpoint):\n",
     "    return pd.DataFrame(results_final[endpoint][0]).applymap(lambda x: x[0]).T.sort_values('f1', ascending=False)\n",
@@ -3351,7 +13040,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 291,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3375,7 +13064,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 292,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3402,9 +13091,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 293,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-0.498515545827989, array([-0.49851555]))"
+      ]
+     },
+     "execution_count": 293,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "log10_pvalue[0][0], log10_pvalue[0] # one of the two"
    ]
@@ -3418,9 +13118,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 294,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['F2_marker_ft_Logistic.csv',\n",
+       " 'F3_marker_elf_Logistic.csv',\n",
+       " 'F3_marker_forns_Logistic.csv',\n",
+       " 'F3_marker_swe_Logistic.csv',\n",
+       " 'I2_marker_alt_Logistic.csv',\n",
+       " 'F2_marker_p3np_Logistic.csv',\n",
+       " 'S1_prot_Logistic.csv',\n",
+       " 'F3_marker_p3np_Logistic.csv',\n",
+       " 'S1_marker_cap_Logistic.csv',\n",
+       " 'I2_marker_ast_Logistic.csv',\n",
+       " 'I2_marker_aar_Logistic.csv',\n",
+       " 'I2_prot_Logistic.csv',\n",
+       " 'I2_marker_m30_Logistic.csv',\n",
+       " 'I2_marker_m65_Logistic.csv',\n",
+       " 'I2_marker_m30m65_ratio_Logistic.csv',\n",
+       " 'F3_marker_ft_Logistic.csv',\n",
+       " 'F2_prot_Logistic.csv',\n",
+       " 'F2_marker_te_Logistic.csv',\n",
+       " 'F2_marker_apri_Logistic.csv',\n",
+       " 'F2_marker_swe_Logistic.csv',\n",
+       " 'F3_prot_Logistic.csv',\n",
+       " 'F3_marker_fib4_Logistic.csv',\n",
+       " 'F2_marker_elf_Logistic.csv',\n",
+       " 'F2_marker_fib4_Logistic.csv',\n",
+       " 'F2_marker_forns_Logistic.csv',\n",
+       " 'F3_marker_apri_Logistic.csv',\n",
+       " 'F3_marker_te_Logistic.csv']"
+      ]
+     },
+     "execution_count": 294,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "folder_final_scores = os.path.join(TABLEFOLDER, FOLDER_FINAL_SCORES)\n",
     "l_scores = [_csv for _csv in os.listdir(folder_final_scores) if 'Logistic.csv' in _csv]\n",
@@ -3436,7 +13173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 295,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3453,9 +13190,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 296,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Compare F2 marker ft Logistic to S1 marker cap Logistic\n",
+      "\n",
+      "INFO:root:Omitting 30 from test set of F2 marker ft Logistic: Plate1_A4, Plate1_B5, Plate1_B8, Plate2_C9, Plate2_E9, Plate2_F12, Plate2_G4, Plate3_A4, Plate3_A5, Plate3_E11, Plate3_G9, Plate3_H9, Plate4_A7, Plate4_B10, Plate4_C3, Plate4_D7, Plate4_E12, Plate4_E9, Plate4_H9, Plate5_C10, Plate5_F10, Plate5_F2, Plate5_H10, Plate6_A5, Plate6_D2, Plate6_D7, Plate6_F1, Plate6_H2, Plate7_B9, Plate7_C10\n",
+      "\n",
+      "INFO:root:Omitting 15 from test set of S1 marker cap Logistic: Plate1_D4, Plate1_G2, Plate1_G6, Plate2_C11, Plate2_D2, Plate2_E8, Plate2_F7, Plate2_H5, Plate3_B6, Plate3_G5, Plate4_B9, Plate4_H7, Plate5_H6, Plate6_C2, Plate7_C3\n",
+      "\n",
+      "INFO:root:Comparison based on 24 in total, which are: Plate1_A5, Plate1_C1, Plate2_B7, Plate3_B7, Plate3_C7, Plate3_D5, Plate4_B6, Plate4_D2, Plate4_F7, Plate4_G4, Plate5_B1, Plate5_C11, Plate5_D4, Plate5_F8, Plate5_G8, Plate6_C7, Plate6_E4, Plate6_E6, Plate6_G1, Plate7_A12, Plate7_A9, Plate7_B11, Plate7_C1, Plate7_C6\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Delong-Test p-value between scores of model F2_marker_ft_Logistic and S1_marker_cap_Logistic: 0.6790\n"
+     ]
+    }
+   ],
    "source": [
     "from src.delong import calc_p_value_delong_xu\n",
     "\n",
@@ -3469,9 +13227,376 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 297,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow0_col0,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow0_col1,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow0_col2,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow0_col3,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow0_col4,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow0_col7,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow1_col0,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow1_col1,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow1_col2,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow1_col3,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow1_col4,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow1_col7,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow2_col0,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow2_col1,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow2_col2,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow2_col3,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow2_col4,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow2_col5,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow2_col7,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow3_col0,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow3_col1,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow3_col2,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow3_col3,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow3_col4,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow3_col5,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow3_col7,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow4_col0,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow4_col1,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow4_col2,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow4_col3,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow4_col4,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow4_col5,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow4_col7,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow5_col2,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow5_col3,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow5_col4,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow5_col5,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow5_col6,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow5_col7,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow5_col8,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow6_col5,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow6_col6,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow6_col8,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow7_col0,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow7_col1,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow7_col2,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow7_col3,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow7_col4,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow7_col5,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow7_col7,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow8_col5,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow8_col6,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow8_col8{\n",
+       "            color:  black;\n",
+       "        }#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow0_col5,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow0_col6,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow0_col8,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow1_col5,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow1_col6,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow1_col8,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow2_col6,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow2_col8,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow3_col6,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow3_col8,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow4_col6,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow4_col8,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow5_col0,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow5_col1,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow6_col0,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow6_col1,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow6_col2,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow6_col3,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow6_col4,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow6_col7,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow7_col6,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow7_col8,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow8_col0,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow8_col1,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow8_col2,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow8_col3,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow8_col4,#T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow8_col7{\n",
+       "            color:  red;\n",
+       "        }</style><table id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43b\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >F2 prot Logistic</th>        <th class=\"col_heading level0 col1\" >F2 marker swe Logistic</th>        <th class=\"col_heading level0 col2\" >F2 marker elf Logistic</th>        <th class=\"col_heading level0 col3\" >F2 marker p3np Logistic</th>        <th class=\"col_heading level0 col4\" >F2 marker te Logistic</th>        <th class=\"col_heading level0 col5\" >F2 marker forns Logistic</th>        <th class=\"col_heading level0 col6\" >F2 marker apri Logistic</th>        <th class=\"col_heading level0 col7\" >F2 marker ft Logistic</th>        <th class=\"col_heading level0 col8\" >F2 marker fib4 Logistic</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                        <th id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43blevel0_row0\" class=\"row_heading level0 row0\" >F2 prot Logistic</th>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow0_col1\" class=\"data row0 col1\" >0.395</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow0_col2\" class=\"data row0 col2\" >0.116</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow0_col3\" class=\"data row0 col3\" >0.176</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow0_col4\" class=\"data row0 col4\" >0.174</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow0_col5\" class=\"data row0 col5\" >0.016</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow0_col6\" class=\"data row0 col6\" >0.002</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow0_col7\" class=\"data row0 col7\" >0.092</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow0_col8\" class=\"data row0 col8\" >0.000</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43blevel0_row1\" class=\"row_heading level0 row1\" >F2 marker swe Logistic</th>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow1_col0\" class=\"data row1 col0\" >0.395</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow1_col2\" class=\"data row1 col2\" >0.380</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow1_col3\" class=\"data row1 col3\" >0.552</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow1_col4\" class=\"data row1 col4\" >0.553</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow1_col5\" class=\"data row1 col5\" >0.029</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow1_col6\" class=\"data row1 col6\" >0.007</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow1_col7\" class=\"data row1 col7\" >0.085</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow1_col8\" class=\"data row1 col8\" >0.002</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43blevel0_row2\" class=\"row_heading level0 row2\" >F2 marker elf Logistic</th>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow2_col0\" class=\"data row2 col0\" >0.116</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow2_col1\" class=\"data row2 col1\" >0.380</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow2_col3\" class=\"data row2 col3\" >0.852</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow2_col4\" class=\"data row2 col4\" >0.738</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow2_col5\" class=\"data row2 col5\" >0.136</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow2_col6\" class=\"data row2 col6\" >0.043</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow2_col7\" class=\"data row2 col7\" >0.746</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow2_col8\" class=\"data row2 col8\" >0.002</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43blevel0_row3\" class=\"row_heading level0 row3\" >F2 marker p3np Logistic</th>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow3_col0\" class=\"data row3 col0\" >0.176</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow3_col1\" class=\"data row3 col1\" >0.552</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow3_col2\" class=\"data row3 col2\" >0.852</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow3_col4\" class=\"data row3 col4\" >0.636</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow3_col5\" class=\"data row3 col5\" >0.151</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow3_col6\" class=\"data row3 col6\" >0.041</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow3_col7\" class=\"data row3 col7\" >0.657</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow3_col8\" class=\"data row3 col8\" >0.008</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43blevel0_row4\" class=\"row_heading level0 row4\" >F2 marker te Logistic</th>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow4_col0\" class=\"data row4 col0\" >0.174</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow4_col1\" class=\"data row4 col1\" >0.553</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow4_col2\" class=\"data row4 col2\" >0.738</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow4_col3\" class=\"data row4 col3\" >0.636</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow4_col5\" class=\"data row4 col5\" >0.099</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow4_col6\" class=\"data row4 col6\" >0.017</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow4_col7\" class=\"data row4 col7\" >0.246</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow4_col8\" class=\"data row4 col8\" >0.005</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43blevel0_row5\" class=\"row_heading level0 row5\" >F2 marker forns Logistic</th>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow5_col0\" class=\"data row5 col0\" >0.016</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow5_col1\" class=\"data row5 col1\" >0.029</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow5_col2\" class=\"data row5 col2\" >0.136</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow5_col3\" class=\"data row5 col3\" >0.151</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow5_col4\" class=\"data row5 col4\" >0.099</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow5_col6\" class=\"data row5 col6\" >0.555</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow5_col7\" class=\"data row5 col7\" >0.137</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow5_col8\" class=\"data row5 col8\" >0.166</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43blevel0_row6\" class=\"row_heading level0 row6\" >F2 marker apri Logistic</th>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow6_col0\" class=\"data row6 col0\" >0.002</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow6_col1\" class=\"data row6 col1\" >0.007</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow6_col2\" class=\"data row6 col2\" >0.043</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow6_col3\" class=\"data row6 col3\" >0.041</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow6_col4\" class=\"data row6 col4\" >0.017</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow6_col5\" class=\"data row6 col5\" >0.555</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow6_col7\" class=\"data row6 col7\" >0.036</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow6_col8\" class=\"data row6 col8\" >0.357</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43blevel0_row7\" class=\"row_heading level0 row7\" >F2 marker ft Logistic</th>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow7_col0\" class=\"data row7 col0\" >0.092</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow7_col1\" class=\"data row7 col1\" >0.085</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow7_col2\" class=\"data row7 col2\" >0.746</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow7_col3\" class=\"data row7 col3\" >0.657</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow7_col4\" class=\"data row7 col4\" >0.246</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow7_col5\" class=\"data row7 col5\" >0.137</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow7_col6\" class=\"data row7 col6\" >0.036</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow7_col8\" class=\"data row7 col8\" >0.006</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43blevel0_row8\" class=\"row_heading level0 row8\" >F2 marker fib4 Logistic</th>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow8_col0\" class=\"data row8 col0\" >0.000</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow8_col1\" class=\"data row8 col1\" >0.002</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow8_col2\" class=\"data row8 col2\" >0.002</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow8_col3\" class=\"data row8 col3\" >0.008</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow8_col4\" class=\"data row8 col4\" >0.005</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow8_col5\" class=\"data row8 col5\" >0.166</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow8_col6\" class=\"data row8 col6\" >0.357</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow8_col7\" class=\"data row8 col7\" >0.006</td>\n",
+       "                        <td id=\"T_dd7cc224_2dfd_11ec_b7fa_367ddaa5d43brow8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f9ef0b6cbd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow0_col0,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow0_col1,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow0_col2,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow0_col3,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow1_col0,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow1_col1,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow1_col2,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow1_col3,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow1_col4,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow1_col5,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow1_col6,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow1_col7,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow2_col0,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow2_col1,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow2_col2,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow2_col3,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow2_col4,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow3_col0,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow3_col1,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow3_col2,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow3_col3,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow3_col4,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow3_col5,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow3_col6,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow3_col7,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow3_col8,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow4_col1,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow4_col2,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow4_col3,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow4_col4,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow4_col5,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow4_col6,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow4_col7,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow4_col8,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow5_col1,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow5_col3,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow5_col4,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow5_col5,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow5_col6,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow5_col7,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow5_col8,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow6_col1,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow6_col3,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow6_col4,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow6_col5,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow6_col6,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow6_col7,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow6_col8,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow7_col1,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow7_col3,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow7_col4,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow7_col5,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow7_col6,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow7_col7,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow7_col8,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow8_col3,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow8_col4,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow8_col5,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow8_col6,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow8_col7,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow8_col8{\n",
+       "            color:  black;\n",
+       "        }#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow0_col4,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow0_col5,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow0_col6,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow0_col7,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow0_col8,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow1_col8,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow2_col5,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow2_col6,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow2_col7,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow2_col8,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow4_col0,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow5_col0,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow5_col2,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow6_col0,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow6_col2,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow7_col0,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow7_col2,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow8_col0,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow8_col1,#T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow8_col2{\n",
+       "            color:  red;\n",
+       "        }</style><table id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43b\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >F3 prot Logistic</th>        <th class=\"col_heading level0 col1\" >F3 marker swe Logistic</th>        <th class=\"col_heading level0 col2\" >F3 marker te Logistic</th>        <th class=\"col_heading level0 col3\" >F3 marker elf Logistic</th>        <th class=\"col_heading level0 col4\" >F3 marker ft Logistic</th>        <th class=\"col_heading level0 col5\" >F3 marker forns Logistic</th>        <th class=\"col_heading level0 col6\" >F3 marker p3np Logistic</th>        <th class=\"col_heading level0 col7\" >F3 marker fib4 Logistic</th>        <th class=\"col_heading level0 col8\" >F3 marker apri Logistic</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                        <th id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43blevel0_row0\" class=\"row_heading level0 row0\" >F3 prot Logistic</th>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow0_col1\" class=\"data row0 col1\" >0.831</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow0_col2\" class=\"data row0 col2\" >0.245</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow0_col3\" class=\"data row0 col3\" >0.127</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow0_col4\" class=\"data row0 col4\" >0.037</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow0_col5\" class=\"data row0 col5\" >0.041</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow0_col6\" class=\"data row0 col6\" >0.025</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow0_col7\" class=\"data row0 col7\" >0.049</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow0_col8\" class=\"data row0 col8\" >0.027</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43blevel0_row1\" class=\"row_heading level0 row1\" >F3 marker swe Logistic</th>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow1_col0\" class=\"data row1 col0\" >0.831</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow1_col2\" class=\"data row1 col2\" >0.700</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow1_col3\" class=\"data row1 col3\" >0.472</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow1_col4\" class=\"data row1 col4\" >0.167</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow1_col5\" class=\"data row1 col5\" >0.063</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow1_col6\" class=\"data row1 col6\" >0.213</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow1_col7\" class=\"data row1 col7\" >0.061</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow1_col8\" class=\"data row1 col8\" >0.039</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43blevel0_row2\" class=\"row_heading level0 row2\" >F3 marker te Logistic</th>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow2_col0\" class=\"data row2 col0\" >0.245</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow2_col1\" class=\"data row2 col1\" >0.700</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow2_col3\" class=\"data row2 col3\" >0.056</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow2_col4\" class=\"data row2 col4\" >0.051</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow2_col5\" class=\"data row2 col5\" >0.026</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow2_col6\" class=\"data row2 col6\" >0.022</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow2_col7\" class=\"data row2 col7\" >0.040</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow2_col8\" class=\"data row2 col8\" >0.019</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43blevel0_row3\" class=\"row_heading level0 row3\" >F3 marker elf Logistic</th>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow3_col0\" class=\"data row3 col0\" >0.127</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow3_col1\" class=\"data row3 col1\" >0.472</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow3_col2\" class=\"data row3 col2\" >0.056</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow3_col4\" class=\"data row3 col4\" >0.141</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow3_col5\" class=\"data row3 col5\" >0.111</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow3_col6\" class=\"data row3 col6\" >0.197</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow3_col7\" class=\"data row3 col7\" >0.133</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow3_col8\" class=\"data row3 col8\" >0.067</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43blevel0_row4\" class=\"row_heading level0 row4\" >F3 marker ft Logistic</th>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow4_col0\" class=\"data row4 col0\" >0.037</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow4_col1\" class=\"data row4 col1\" >0.167</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow4_col2\" class=\"data row4 col2\" >0.051</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow4_col3\" class=\"data row4 col3\" >0.141</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow4_col5\" class=\"data row4 col5\" >0.238</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow4_col6\" class=\"data row4 col6\" >0.434</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow4_col7\" class=\"data row4 col7\" >0.231</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow4_col8\" class=\"data row4 col8\" >0.115</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43blevel0_row5\" class=\"row_heading level0 row5\" >F3 marker forns Logistic</th>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow5_col0\" class=\"data row5 col0\" >0.041</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow5_col1\" class=\"data row5 col1\" >0.063</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow5_col2\" class=\"data row5 col2\" >0.026</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow5_col3\" class=\"data row5 col3\" >0.111</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow5_col4\" class=\"data row5 col4\" >0.238</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow5_col6\" class=\"data row5 col6\" >0.279</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow5_col7\" class=\"data row5 col7\" >0.668</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow5_col8\" class=\"data row5 col8\" >0.414</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43blevel0_row6\" class=\"row_heading level0 row6\" >F3 marker p3np Logistic</th>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow6_col0\" class=\"data row6 col0\" >0.025</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow6_col1\" class=\"data row6 col1\" >0.213</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow6_col2\" class=\"data row6 col2\" >0.022</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow6_col3\" class=\"data row6 col3\" >0.197</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow6_col4\" class=\"data row6 col4\" >0.434</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow6_col5\" class=\"data row6 col5\" >0.279</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow6_col7\" class=\"data row6 col7\" >0.283</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow6_col8\" class=\"data row6 col8\" >0.142</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43blevel0_row7\" class=\"row_heading level0 row7\" >F3 marker fib4 Logistic</th>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow7_col0\" class=\"data row7 col0\" >0.049</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow7_col1\" class=\"data row7 col1\" >0.061</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow7_col2\" class=\"data row7 col2\" >0.040</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow7_col3\" class=\"data row7 col3\" >0.133</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow7_col4\" class=\"data row7 col4\" >0.231</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow7_col5\" class=\"data row7 col5\" >0.668</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow7_col6\" class=\"data row7 col6\" >0.283</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow7_col8\" class=\"data row7 col8\" >0.105</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43blevel0_row8\" class=\"row_heading level0 row8\" >F3 marker apri Logistic</th>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow8_col0\" class=\"data row8 col0\" >0.027</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow8_col1\" class=\"data row8 col1\" >0.039</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow8_col2\" class=\"data row8 col2\" >0.019</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow8_col3\" class=\"data row8 col3\" >0.067</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow8_col4\" class=\"data row8 col4\" >0.115</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow8_col5\" class=\"data row8 col5\" >0.414</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow8_col6\" class=\"data row8 col6\" >0.142</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow8_col7\" class=\"data row8 col7\" >0.105</td>\n",
+       "                        <td id=\"T_ddb146b6_2dfd_11ec_b7fa_367ddaa5d43brow8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f9f4125e4d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "#T_ddb49974_2dfd_11ec_b7fa_367ddaa5d43brow0_col0,#T_ddb49974_2dfd_11ec_b7fa_367ddaa5d43brow0_col1,#T_ddb49974_2dfd_11ec_b7fa_367ddaa5d43brow1_col0,#T_ddb49974_2dfd_11ec_b7fa_367ddaa5d43brow1_col1{\n",
+       "            color:  black;\n",
+       "        }</style><table id=\"T_ddb49974_2dfd_11ec_b7fa_367ddaa5d43b\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >S1 marker cap Logistic</th>        <th class=\"col_heading level0 col1\" >S1 prot Logistic</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                        <th id=\"T_ddb49974_2dfd_11ec_b7fa_367ddaa5d43blevel0_row0\" class=\"row_heading level0 row0\" >S1 marker cap Logistic</th>\n",
+       "                        <td id=\"T_ddb49974_2dfd_11ec_b7fa_367ddaa5d43brow0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "                        <td id=\"T_ddb49974_2dfd_11ec_b7fa_367ddaa5d43brow0_col1\" class=\"data row0 col1\" >0.490</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ddb49974_2dfd_11ec_b7fa_367ddaa5d43blevel0_row1\" class=\"row_heading level0 row1\" >S1 prot Logistic</th>\n",
+       "                        <td id=\"T_ddb49974_2dfd_11ec_b7fa_367ddaa5d43brow1_col0\" class=\"data row1 col0\" >0.490</td>\n",
+       "                        <td id=\"T_ddb49974_2dfd_11ec_b7fa_367ddaa5d43brow1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f9f11401dd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow0_col0,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow0_col1,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow0_col2,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow0_col3,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow0_col6,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow1_col0,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow1_col1,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow1_col2,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow1_col3,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow2_col0,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow2_col1,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow2_col2,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow2_col3,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow2_col4,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow3_col0,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow3_col1,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow3_col2,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow3_col3,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow3_col4,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow3_col5,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow3_col6,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow4_col2,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow4_col3,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow4_col4,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow4_col5,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow4_col6,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow5_col3,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow5_col4,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow5_col5,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow5_col6,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow6_col0,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow6_col3,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow6_col4,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow6_col5,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow6_col6{\n",
+       "            color:  black;\n",
+       "        }#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow0_col4,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow0_col5,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow1_col4,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow1_col5,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow1_col6,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow2_col5,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow2_col6,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow4_col0,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow4_col1,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow5_col0,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow5_col1,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow5_col2,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow6_col1,#T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow6_col2{\n",
+       "            color:  red;\n",
+       "        }</style><table id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43b\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >I2 marker ast Logistic</th>        <th class=\"col_heading level0 col1\" >I2 prot Logistic</th>        <th class=\"col_heading level0 col2\" >I2 marker m30 Logistic</th>        <th class=\"col_heading level0 col3\" >I2 marker m65 Logistic</th>        <th class=\"col_heading level0 col4\" >I2 marker alt Logistic</th>        <th class=\"col_heading level0 col5\" >I2 marker aar Logistic</th>        <th class=\"col_heading level0 col6\" >I2 marker m30m65 ratio Logistic</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                        <th id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43blevel0_row0\" class=\"row_heading level0 row0\" >I2 marker ast Logistic</th>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow0_col1\" class=\"data row0 col1\" >0.244</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow0_col2\" class=\"data row0 col2\" >0.711</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow0_col3\" class=\"data row0 col3\" >0.621</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow0_col4\" class=\"data row0 col4\" >0.026</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow0_col5\" class=\"data row0 col5\" >0.022</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow0_col6\" class=\"data row0 col6\" >0.063</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43blevel0_row1\" class=\"row_heading level0 row1\" >I2 prot Logistic</th>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow1_col0\" class=\"data row1 col0\" >0.244</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow1_col2\" class=\"data row1 col2\" >0.306</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow1_col3\" class=\"data row1 col3\" >0.096</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow1_col4\" class=\"data row1 col4\" >0.011</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow1_col5\" class=\"data row1 col5\" >0.000</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow1_col6\" class=\"data row1 col6\" >0.014</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43blevel0_row2\" class=\"row_heading level0 row2\" >I2 marker m30 Logistic</th>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow2_col0\" class=\"data row2 col0\" >0.711</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow2_col1\" class=\"data row2 col1\" >0.306</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow2_col3\" class=\"data row2 col3\" >0.269</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow2_col4\" class=\"data row2 col4\" >0.060</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow2_col5\" class=\"data row2 col5\" >0.023</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow2_col6\" class=\"data row2 col6\" >0.036</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43blevel0_row3\" class=\"row_heading level0 row3\" >I2 marker m65 Logistic</th>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow3_col0\" class=\"data row3 col0\" >0.621</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow3_col1\" class=\"data row3 col1\" >0.096</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow3_col2\" class=\"data row3 col2\" >0.269</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow3_col4\" class=\"data row3 col4\" >0.274</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow3_col5\" class=\"data row3 col5\" >0.109</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow3_col6\" class=\"data row3 col6\" >0.274</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43blevel0_row4\" class=\"row_heading level0 row4\" >I2 marker alt Logistic</th>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow4_col0\" class=\"data row4 col0\" >0.026</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow4_col1\" class=\"data row4 col1\" >0.011</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow4_col2\" class=\"data row4 col2\" >0.060</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow4_col3\" class=\"data row4 col3\" >0.274</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow4_col5\" class=\"data row4 col5\" >0.665</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow4_col6\" class=\"data row4 col6\" >0.691</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43blevel0_row5\" class=\"row_heading level0 row5\" >I2 marker aar Logistic</th>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow5_col0\" class=\"data row5 col0\" >0.022</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow5_col1\" class=\"data row5 col1\" >0.000</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow5_col2\" class=\"data row5 col2\" >0.023</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow5_col3\" class=\"data row5 col3\" >0.109</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow5_col4\" class=\"data row5 col4\" >0.665</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow5_col6\" class=\"data row5 col6\" >0.740</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43blevel0_row6\" class=\"row_heading level0 row6\" >I2 marker m30m65 ratio Logistic</th>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow6_col0\" class=\"data row6 col0\" >0.063</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow6_col1\" class=\"data row6 col1\" >0.014</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow6_col2\" class=\"data row6 col2\" >0.036</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow6_col3\" class=\"data row6 col3\" >0.274</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow6_col4\" class=\"data row6 col4\" >0.691</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow6_col5\" class=\"data row6 col5\" >0.740</td>\n",
+       "                        <td id=\"T_ddd230ec_2dfd_11ec_b7fa_367ddaa5d43brow6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f9f220cec10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "auc_comp_dict = {}\n",
     "\n",
@@ -3517,7 +13642,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 298,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3547,9 +13672,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 299,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "NaN    137\n",
+       "1.0    124\n",
+       "2.0    106\n",
+       "0.5     98\n",
+       "4.0     67\n",
+       "0.0     36\n",
+       "3.0     27\n",
+       "Name: kleiner, dtype: int64"
+      ]
+     },
+     "execution_count": 299,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "data_cli_full = pd.read_csv(f_data_clinic, index_col=COL_ID)\n",
     "# previous selection: data_cli[data_cli['kleiner']!=0.5] \n",
@@ -3559,9 +13702,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 300,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "N Healthy (selected to match ill patients: 136\n",
+      "N Unhealthy behaviour, but not sick: 98\n"
+     ]
+    }
+   ],
    "source": [
     "#n_healty, n_at_risk = all_kleiner_score.loc[[np.nan, 0.5]]\n",
     "#One patient in the disease cohort had a 'NaN' kleiner score\n",
@@ -3573,9 +13725,105 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 301,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>kleiner</th>\n",
+       "      <th>nas_steatosis_ordinal</th>\n",
+       "      <th>nas_inflam</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.0</th>\n",
+       "      <td>36.0</td>\n",
+       "      <td>156.0</td>\n",
+       "      <td>72.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.5</th>\n",
+       "      <td>98.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.0</th>\n",
+       "      <td>124.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>91.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2.0</th>\n",
+       "      <td>106.0</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>82.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3.0</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>53.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.0</th>\n",
+       "      <td>67.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5.0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>23.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NaN</th>\n",
+       "      <td>137.0</td>\n",
+       "      <td>243.0</td>\n",
+       "      <td>243.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     kleiner  nas_steatosis_ordinal  nas_inflam\n",
+       "0.0     36.0                  156.0        72.0\n",
+       "0.5     98.0                    NaN         NaN\n",
+       "1.0    124.0                   85.0        91.0\n",
+       "2.0    106.0                   72.0        82.0\n",
+       "3.0     27.0                   39.0        53.0\n",
+       "4.0     67.0                    NaN        31.0\n",
+       "5.0      NaN                    NaN        23.0\n",
+       "NaN    137.0                  243.0       243.0"
+      ]
+     },
+     "execution_count": 301,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# from src.pandas import combine_value_counts\n",
     "combine_value_counts(data_cli_full[TARGETS], dropna=False)"
@@ -3583,18 +13831,199 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 302,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>kleiner</th>\n",
+       "      <th>nas_steatosis_ordinal</th>\n",
+       "      <th>nas_inflam</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.5</th>\n",
+       "      <td>98.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NaN</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>98.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     kleiner  nas_steatosis_ordinal  nas_inflam\n",
+       "0.5     98.0                    NaN         NaN\n",
+       "NaN      NaN                   98.0        98.0"
+      ]
+     },
+     "execution_count": 302,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "combine_value_counts(data_cli_full.loc[data_cli_full.kleiner == 0.5, TARGETS], dropna=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 303,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>kleiner</th>\n",
+       "      <th>nas_steatosis_ordinal</th>\n",
+       "      <th>nas_inflam</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sample ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Plate5_D10</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plate7_A10</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plate6_E10</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plate3_F8</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plate5_H3</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plate4_B1</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plate5_E4</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plate5_D9</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plate1_G9</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plate6_E12</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>98 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            kleiner  nas_steatosis_ordinal  nas_inflam\n",
+       "Sample ID                                             \n",
+       "Plate5_D10      0.5                    NaN         NaN\n",
+       "Plate7_A10      0.5                    NaN         NaN\n",
+       "Plate6_E10      0.5                    NaN         NaN\n",
+       "Plate3_F8       0.5                    NaN         NaN\n",
+       "Plate5_H3       0.5                    NaN         NaN\n",
+       "...             ...                    ...         ...\n",
+       "Plate4_B1       0.5                    NaN         NaN\n",
+       "Plate5_E4       0.5                    NaN         NaN\n",
+       "Plate5_D9       0.5                    NaN         NaN\n",
+       "Plate1_G9       0.5                    NaN         NaN\n",
+       "Plate6_E12      0.5                    NaN         NaN\n",
+       "\n",
+       "[98 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 303,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "data_cli_full.loc[data_cli_full.kleiner == 0.5, TARGETS]"
    ]
@@ -3608,9 +14037,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 304,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Load model from : tables/final_model_scores/F2_prot_Logistic.joblib\n",
+      "[[ 1.41798639e+00  4.18964454e-01  9.82502637e-02 -1.08500592e+00\n",
+      "  -2.24547398e-01 -1.98394585e-01  3.50164840e-02 -4.65453595e-02\n",
+      "   1.05164163e-03 -4.95613743e-01  3.84363240e-02  7.56180249e-02\n",
+      "  -5.10002582e-01  8.86610717e-01]]\n",
+      "Load model from : tables/final_model_scores/F3_prot_Logistic.joblib\n",
+      "[[ 1.45678516  0.34717498  0.92416205  0.20238915  0.31690318  0.58050019\n",
+      "   0.2373801  -0.70993597 -1.04718132  0.92410492  0.49338344 -0.58118158\n",
+      "  -0.6957398   0.5310984   0.45998014 -0.58565233  0.1857521  -0.91728771\n",
+      "  -0.36029818 -0.75561192 -0.51659093]]\n",
+      "Load model from : tables/final_model_scores/I2_prot_Logistic.joblib\n",
+      "[[ 0.96268483  0.48543315  0.27697776  0.34045789  0.06780311 -0.72140567\n",
+      "  -0.81384169 -0.02007811 -0.32780745]]\n",
+      "Load model from : tables/final_model_scores/S1_prot_Logistic.joblib\n",
+      "[[ 0.43672949  0.17323409  0.14436194 -0.55100988 -0.02418889 -0.1307419\n",
+      "   0.51593668  0.16143642 -0.06773431  0.34761108  0.01371284 -0.20659015\n",
+      "   1.18923764 -0.32451293 -1.17681051 -0.86192286 -0.07321465 -0.99238551\n",
+      "   0.79790382 -0.05943388  0.21556779  0.15397331 -0.06398469  1.25344152\n",
+      "   0.10139884 -0.28982287  0.86316386 -0.67391102]]\n"
+     ]
+    }
+   ],
    "source": [
     "from joblib import load\n",
     "endpoints = ['F2', 'F3', 'I2', 'S1']\n",
@@ -3632,16 +14087,108 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 305,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Protein ID</th>\n",
+       "      <th>P05062</th>\n",
+       "      <th>Q08380</th>\n",
+       "      <th>H7BY64</th>\n",
+       "      <th>P06396</th>\n",
+       "      <th>C9JPQ9</th>\n",
+       "      <th>P19320</th>\n",
+       "      <th>Q15582</th>\n",
+       "      <th>P55103</th>\n",
+       "      <th>P08519</th>\n",
+       "      <th>Q92820</th>\n",
+       "      <th>...</th>\n",
+       "      <th>P43652</th>\n",
+       "      <th>P04196</th>\n",
+       "      <th>Q9Y5Y7</th>\n",
+       "      <th>B0YIW2</th>\n",
+       "      <th>P80748</th>\n",
+       "      <th>A0A0U1RR20</th>\n",
+       "      <th>P09172</th>\n",
+       "      <th>P01009</th>\n",
+       "      <th>P0C0L5</th>\n",
+       "      <th>A0A182DWH7</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>S1</th>\n",
+       "      <td>ALDOB</td>\n",
+       "      <td>LGALS3BP</td>\n",
+       "      <td>NoGene</td>\n",
+       "      <td>GSN</td>\n",
+       "      <td>FGG</td>\n",
+       "      <td>VCAM1</td>\n",
+       "      <td>TGFBI</td>\n",
+       "      <td>INHBC</td>\n",
+       "      <td>LPA</td>\n",
+       "      <td>GGH</td>\n",
+       "      <td>...</td>\n",
+       "      <td>AFM</td>\n",
+       "      <td>HRG</td>\n",
+       "      <td>LYVE1</td>\n",
+       "      <td>APOC3</td>\n",
+       "      <td>IGLV3-21</td>\n",
+       "      <td>PRG4</td>\n",
+       "      <td>DBH</td>\n",
+       "      <td>SERPINA1</td>\n",
+       "      <td>C4B</td>\n",
+       "      <td>SELENOP</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Protein ID P05062    Q08380  H7BY64 P06396 C9JPQ9 P19320 Q15582 P55103 P08519  \\\n",
+       "S1          ALDOB  LGALS3BP  NoGene    GSN    FGG  VCAM1  TGFBI  INHBC    LPA   \n",
+       "\n",
+       "Protein ID Q92820  ... P43652 P04196 Q9Y5Y7 B0YIW2    P80748 A0A0U1RR20  \\\n",
+       "S1            GGH  ...    AFM    HRG  LYVE1  APOC3  IGLV3-21       PRG4   \n",
+       "\n",
+       "Protein ID P09172    P01009 P0C0L5 A0A182DWH7  \n",
+       "S1            DBH  SERPINA1    C4B    SELENOP  \n",
+       "\n",
+       "[1 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 305,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "arguments['S1']['proteins'].T"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 306,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3651,7 +14198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 307,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3673,9 +14220,458 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 308,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>F2</th>\n",
+       "      <th>F3</th>\n",
+       "      <th>I2</th>\n",
+       "      <th>S1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>SELENOP</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.674</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C4B</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.863</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SERPINA1</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DBH</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PRG4</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.253</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>IGLV3-21</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.064</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>APOC3</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HRG</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AFM</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.798</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ITIH4</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.992</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GNPTG</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.073</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CDH5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.862</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GC</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.177</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FETUB</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.325</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PEPD</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CFHR4</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.207</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GGH</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.348</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LPA</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.068</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>INHBC</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.161</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FGG</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GSN</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.551</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nan</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ALDOB</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.437</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ICAM1</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.020</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FBLN1</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.814</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CLU</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.721</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>IGLC2</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.517</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FCN2</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.360</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>APOC1</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.917</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>IGKV3-20</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.186</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>IGKC</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.460</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SERPIND1</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.581</td>\n",
+       "      <td>-0.328</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>COL6A3</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.493</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LUM</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.924</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PON1</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.710</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>IGHA1</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.237</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>IGFALS</th>\n",
+       "      <td>-1.085</td>\n",
+       "      <td>-0.586</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LYVE1</th>\n",
+       "      <td>-0.510</td>\n",
+       "      <td>-1.047</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.216</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HPR</th>\n",
+       "      <td>-0.496</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>APCS</th>\n",
+       "      <td>-0.225</td>\n",
+       "      <td>0.202</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>IGHA2</th>\n",
+       "      <td>-0.198</td>\n",
+       "      <td>-0.696</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>intercept</th>\n",
+       "      <td>-0.153</td>\n",
+       "      <td>-0.008</td>\n",
+       "      <td>-0.154</td>\n",
+       "      <td>-0.021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LGALS3BP</th>\n",
+       "      <td>-0.047</td>\n",
+       "      <td>0.581</td>\n",
+       "      <td>0.963</td>\n",
+       "      <td>0.173</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PIGR</th>\n",
+       "      <td>0.001</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.277</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>QSOX1</th>\n",
+       "      <td>0.035</td>\n",
+       "      <td>0.924</td>\n",
+       "      <td>0.068</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>IGKV3D-11</th>\n",
+       "      <td>0.038</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>OSMR</th>\n",
+       "      <td>0.076</td>\n",
+       "      <td>0.531</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>IGFBP7</th>\n",
+       "      <td>0.098</td>\n",
+       "      <td>0.317</td>\n",
+       "      <td>0.340</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>VCAM1</th>\n",
+       "      <td>0.419</td>\n",
+       "      <td>0.347</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TGFBI</th>\n",
+       "      <td>0.887</td>\n",
+       "      <td>-0.756</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C7</th>\n",
+       "      <td>1.418</td>\n",
+       "      <td>1.457</td>\n",
+       "      <td>0.485</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              F2     F3     I2     S1\n",
+       "SELENOP      NaN    NaN    NaN -0.674\n",
+       "C4B          NaN    NaN    NaN  0.863\n",
+       "SERPINA1     NaN    NaN    NaN -0.290\n",
+       "DBH          NaN    NaN    NaN  0.101\n",
+       "PRG4         NaN    NaN    NaN  1.253\n",
+       "IGLV3-21     NaN    NaN    NaN -0.064\n",
+       "APOC3        NaN    NaN    NaN  0.154\n",
+       "HRG          NaN    NaN    NaN -0.059\n",
+       "AFM          NaN    NaN    NaN  0.798\n",
+       "ITIH4        NaN    NaN    NaN -0.992\n",
+       "GNPTG        NaN    NaN    NaN -0.073\n",
+       "CDH5         NaN    NaN    NaN -0.862\n",
+       "GC           NaN    NaN    NaN -1.177\n",
+       "FETUB        NaN    NaN    NaN -0.325\n",
+       "PEPD         NaN    NaN    NaN  1.189\n",
+       "CFHR4        NaN    NaN    NaN -0.207\n",
+       "GGH          NaN    NaN    NaN  0.348\n",
+       "LPA          NaN    NaN    NaN -0.068\n",
+       "INHBC        NaN    NaN    NaN  0.161\n",
+       "FGG          NaN    NaN    NaN -0.024\n",
+       "GSN          NaN    NaN    NaN -0.551\n",
+       "nan          NaN    NaN    NaN  0.144\n",
+       "ALDOB        NaN    NaN    NaN  0.437\n",
+       "ICAM1        NaN    NaN -0.020    NaN\n",
+       "FBLN1        NaN    NaN -0.814    NaN\n",
+       "CLU          NaN    NaN -0.721    NaN\n",
+       "IGLC2        NaN -0.517    NaN    NaN\n",
+       "FCN2         NaN -0.360    NaN    NaN\n",
+       "APOC1        NaN -0.917    NaN    NaN\n",
+       "IGKV3-20     NaN  0.186    NaN    NaN\n",
+       "IGKC         NaN  0.460    NaN    NaN\n",
+       "SERPIND1     NaN -0.581 -0.328    NaN\n",
+       "COL6A3       NaN  0.493    NaN    NaN\n",
+       "LUM          NaN  0.924    NaN    NaN\n",
+       "PON1         NaN -0.710    NaN    NaN\n",
+       "IGHA1        NaN  0.237    NaN    NaN\n",
+       "IGFALS    -1.085 -0.586    NaN  0.014\n",
+       "LYVE1     -0.510 -1.047    NaN  0.216\n",
+       "HPR       -0.496    NaN    NaN    NaN\n",
+       "APCS      -0.225  0.202    NaN    NaN\n",
+       "IGHA2     -0.198 -0.696    NaN    NaN\n",
+       "intercept -0.153 -0.008 -0.154 -0.021\n",
+       "LGALS3BP  -0.047  0.581  0.963  0.173\n",
+       "PIGR       0.001    NaN  0.277    NaN\n",
+       "QSOX1      0.035  0.924  0.068    NaN\n",
+       "IGKV3D-11  0.038    NaN    NaN    NaN\n",
+       "OSMR       0.076  0.531    NaN    NaN\n",
+       "IGFBP7     0.098  0.317  0.340    NaN\n",
+       "VCAM1      0.419  0.347    NaN -0.131\n",
+       "TGFBI      0.887 -0.756    NaN  0.516\n",
+       "C7         1.418  1.457  0.485    NaN"
+      ]
+     },
+     "execution_count": 308,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# protein panel. Some order?\n",
     "d_model_weights = {}\n",
@@ -3706,9 +14702,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 309,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 216x1080 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ax = model_weights.plot(kind='barh', figsize=(3,15), color=['darkblue', 'darkred', 'gray'])\n",
     "plt.xticks(fontsize=14);\n",
@@ -3728,7 +14737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 310,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3738,7 +14747,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 311,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3750,7 +14759,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 312,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3766,9 +14775,98 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 313,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>F2</th>\n",
+       "      <th>F3</th>\n",
+       "      <th>I2</th>\n",
+       "      <th>S1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sample ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Plate4_C2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plate2_C1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plate5_H2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plate5_C8</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Plate4_H6</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           F2  F3  I2  S1\n",
+       "Sample ID                \n",
+       "Plate4_C2   1   0   1   1\n",
+       "Plate2_C1   1   0   1   1\n",
+       "Plate5_H2   1   0   0   1\n",
+       "Plate5_C8   1   0   0   0\n",
+       "Plate4_H6   1   0   1   1"
+      ]
+     },
+     "execution_count": 313,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "at_risk_score = at_risk_score.sort_values(by=list(at_risk_score.columns), ascending=False).style.applymap(highlight_surpassed_score)\n",
     "at_risk_pred = at_risk_pred.loc[at_risk_score.index]\n",
@@ -3777,9 +14875,90 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 314,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>F2</th>\n",
+       "      <th>F3</th>\n",
+       "      <th>I2</th>\n",
+       "      <th>S1</th>\n",
+       "      <th>F2_freq</th>\n",
+       "      <th>F3_freq</th>\n",
+       "      <th>I2_freq</th>\n",
+       "      <th>S1_freq</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>86</td>\n",
+       "      <td>97</td>\n",
+       "      <td>81</td>\n",
+       "      <td>51</td>\n",
+       "      <td>0.887</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.835</td>\n",
+       "      <td>0.526</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>11</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>16</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0.113</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>0.165</td>\n",
+       "      <td>0.474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total</th>\n",
+       "      <td>97</td>\n",
+       "      <td>97</td>\n",
+       "      <td>97</td>\n",
+       "      <td>97</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>1.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       F2    F3  I2  S1  F2_freq  F3_freq  I2_freq  S1_freq\n",
+       "0      86    97  81  51    0.887        1    0.835    0.526\n",
+       "1      11  <NA>  16  46    0.113     <NA>    0.165    0.474\n",
+       "Total  97    97  97  97    1.000        1    1.000    1.000"
+      ]
+     },
+     "execution_count": 314,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "def build_results_table(predictions:pd.DataFrame, dropna=True):\n",
     "    \"\"\"Convert a table of predicitons (for several columns) into a result table.\"\"\"\n",
@@ -3794,9 +14973,98 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 315,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>variable</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">F2</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">F3</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">I2</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">S1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>statistics</th>\n",
+       "      <th>freq</th>\n",
+       "      <th>prop</th>\n",
+       "      <th>freq</th>\n",
+       "      <th>prop</th>\n",
+       "      <th>freq</th>\n",
+       "      <th>prop</th>\n",
+       "      <th>freq</th>\n",
+       "      <th>prop</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>86</td>\n",
+       "      <td>0.887</td>\n",
+       "      <td>97</td>\n",
+       "      <td>1</td>\n",
+       "      <td>81</td>\n",
+       "      <td>0.835</td>\n",
+       "      <td>51</td>\n",
+       "      <td>0.526</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>11</td>\n",
+       "      <td>0.113</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.165</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0.474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total</th>\n",
+       "      <td>97</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>97</td>\n",
+       "      <td>1</td>\n",
+       "      <td>97</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>97</td>\n",
+       "      <td>1.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "variable     F2           F3         I2          S1       \n",
+       "statistics freq   prop  freq  prop freq   prop freq   prop\n",
+       "0            86  0.887    97     1   81  0.835   51  0.526\n",
+       "1            11  0.113  <NA>  <NA>   16  0.165   46  0.474\n",
+       "Total        97  1.000    97     1   97  1.000   97  1.000"
+      ]
+     },
+     "execution_count": 315,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# ToDo: move to src (replace index building in cross_validation._get_cv_means)\n",
     "def build_two_level_index(initial_columns, present_key='freq', added_key='prop'):\n",
@@ -3841,25 +15109,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 316,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "F0-1    160\n",
+       "NaN     137\n",
+       "F2      106\n",
+       "F3-4     94\n",
+       "Name: fibrosis_class, dtype: int64"
+      ]
+     },
+     "execution_count": 316,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "data_cli.fibrosis_class.value_counts(dropna=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 317,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(136, 164)"
+      ]
+     },
+     "execution_count": 317,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "data_cli[data_cli['group']=='HP'].shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 318,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3873,9 +15167,98 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 319,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>variable</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">F2</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">F3</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">I2</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">S1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>statistics</th>\n",
+       "      <th>freq</th>\n",
+       "      <th>prop</th>\n",
+       "      <th>freq</th>\n",
+       "      <th>prop</th>\n",
+       "      <th>freq</th>\n",
+       "      <th>prop</th>\n",
+       "      <th>freq</th>\n",
+       "      <th>prop</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>130</td>\n",
+       "      <td>0.956</td>\n",
+       "      <td>136</td>\n",
+       "      <td>1</td>\n",
+       "      <td>131</td>\n",
+       "      <td>0.963</td>\n",
+       "      <td>112</td>\n",
+       "      <td>0.824</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6</td>\n",
+       "      <td>0.044</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.037</td>\n",
+       "      <td>24</td>\n",
+       "      <td>0.176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total</th>\n",
+       "      <td>136</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>136</td>\n",
+       "      <td>1</td>\n",
+       "      <td>136</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>136</td>\n",
+       "      <td>1.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "variable     F2           F3         I2          S1       \n",
+       "statistics freq   prop  freq  prop freq   prop freq   prop\n",
+       "0           130  0.956   136     1  131  0.963  112  0.824\n",
+       "1             6  0.044  <NA>  <NA>    5  0.037   24  0.176\n",
+       "Total       136  1.000   136     1  136  1.000  136  1.000"
+      ]
+     },
+     "execution_count": 319,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "healthy_results_tab  = build_results_table(healthy_pred)\n",
     "column_map, multi_index = build_two_level_index(initial_columns=healthy_pred.columns)\n",
@@ -3909,7 +15292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 320,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3926,6 +15309,175 @@
     "        _df.to_excel(writer, sheet_name=sheet_name)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Final model performance on test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 333,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "targets_final_test = pd.DataFrame(targets_dict).loc[train_test_split_final[0][1]]\n",
+    "pred_final_test = final_predictor.predict(indices=train_test_split_final[0][1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 334,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "def confusion_matrix_scorer(y, y_pred):\n",
+    "    cm = confusion_matrix(y, y_pred)\n",
+    "    return {'tn': cm[0, 0], 'fp': cm[0, 1],\n",
+    "              'fn': cm[1, 0], 'tp': cm[1, 1]}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 335,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_cases = ['F2','F3', 'I2', 'S1']\n",
+    "cm_scores_final_test = {}\n",
+    "for test_case in test_cases:\n",
+    "    overlap_ids = set(pred_final_test[test_case].dropna().index) & set(targets_final_test[test_case].dropna().index)\n",
+    "    cm_scores_final_test[test_case]=confusion_matrix_scorer(targets_final_test.loc[overlap_ids][test_case], pred_final_test.loc[overlap_ids][test_case]) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 336,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def performance_score_metric(confusion_matrix):\n",
+    "    \"\"\"\n",
+    "    confusion_matrix as a dictionary with keys of 'tn', 'fp', 'fn', 'tp'.\n",
+    "    \"\"\"\n",
+    "    dict_cm =confusion_matrix\n",
+    "    tps = dict_cm['tp']\n",
+    "    fns = dict_cm['fn']\n",
+    "    fps = dict_cm['fp']\n",
+    "    tns = dict_cm['tn']\n",
+    "    PPV = tps/(tps + fps)\n",
+    "    NPV = tns/(tns + fns)\n",
+    "    recall = tps/(tps + fns)\n",
+    "    specificity = tns/(tns + fps)\n",
+    "    balanced_accuracy = (recall + specificity)/2\n",
+    "    f1_score = 2*PPV*recall/(PPV + recall)\n",
+    "    scores = {'PPV': PPV.round(2), 'NPV': NPV.round(2), 'recall':recall.round(2), \n",
+    "              'specificity':specificity.round(2), 'F1 score':f1_score.round(2), 'balanced accuracy':balanced_accuracy.round(2)}\n",
+    "    return(scores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 337,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'PPV': 0.94,\n",
+       " 'NPV': 0.78,\n",
+       " 'recall': 0.76,\n",
+       " 'specificity': 0.94,\n",
+       " 'F1 score': 0.84,\n",
+       " 'balanced accuracy': 0.85}"
+      ]
+     },
+     "execution_count": 337,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "performance_score_metric(cm_scores_final_test['F2'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 338,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'PPV': 0.74,\n",
+       " 'NPV': 0.8,\n",
+       " 'recall': 0.79,\n",
+       " 'specificity': 0.76,\n",
+       " 'F1 score': 0.76,\n",
+       " 'balanced accuracy': 0.77}"
+      ]
+     },
+     "execution_count": 338,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "performance_score_metric(cm_scores_final_test['I2'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 339,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'PPV': 0.85,\n",
+       " 'NPV': 0.77,\n",
+       " 'recall': 0.83,\n",
+       " 'specificity': 0.79,\n",
+       " 'F1 score': 0.84,\n",
+       " 'balanced accuracy': 0.81}"
+      ]
+     },
+     "execution_count": 339,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "performance_score_metric(cm_scores_final_test['S1'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 340,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'PPV': 0.88,\n",
+       " 'NPV': 0.95,\n",
+       " 'recall': 0.83,\n",
+       " 'specificity': 0.96,\n",
+       " 'F1 score': 0.86,\n",
+       " 'balanced accuracy': 0.9}"
+      ]
+     },
+     "execution_count": 340,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "performance_score_metric(cm_scores_final_test['F3'])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -3939,9 +15491,116 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 321,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sample ID</th>\n",
+       "      <th>Participant ID</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Plate6_G11</td>\n",
+       "      <td>ALD_1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Plate1_F7</td>\n",
+       "      <td>ALD_2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Plate6_D2</td>\n",
+       "      <td>ALD_3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Plate6_C5</td>\n",
+       "      <td>ALD_4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Plate4_F8</td>\n",
+       "      <td>ALD_5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>590</th>\n",
+       "      <td>Plate4_B7</td>\n",
+       "      <td>HP_735</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>591</th>\n",
+       "      <td>Plate3_C8</td>\n",
+       "      <td>HP_737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>592</th>\n",
+       "      <td>Plate7_A11</td>\n",
+       "      <td>HP_743</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>593</th>\n",
+       "      <td>Plate3_G11</td>\n",
+       "      <td>HP_746</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>594</th>\n",
+       "      <td>Plate3_F10</td>\n",
+       "      <td>HP_750</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>595 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Sample ID Participant ID\n",
+       "0    Plate6_G11          ALD_1\n",
+       "1     Plate1_F7          ALD_2\n",
+       "2     Plate6_D2          ALD_3\n",
+       "3     Plate6_C5          ALD_4\n",
+       "4     Plate4_F8          ALD_5\n",
+       "..          ...            ...\n",
+       "590   Plate4_B7         HP_735\n",
+       "591   Plate3_C8         HP_737\n",
+       "592  Plate7_A11         HP_743\n",
+       "593  Plate3_G11         HP_746\n",
+       "594  Plate3_F10         HP_750\n",
+       "\n",
+       "[595 rows x 2 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "try:\n",
     "    doubleID=pd.read_csv('data/raw/DoubleIDkey.csv', index_col=False)\n",
@@ -3959,7 +15618,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 322,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3969,9 +15628,601 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 323,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
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+       "            background:  yellow;\n",
+       "        }#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow0_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow2_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow5_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow7_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow9_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow10_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow12_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow13_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow16_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow17_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow19_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow20_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow21_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow23_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow25_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow28_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow30_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow31_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow32_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow33_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow34_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow37_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow40_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow41_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow43_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow44_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow47_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow49_col6,#T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow50_col6{\n",
+       "            background:  pink;\n",
+       "        }</style><table id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43b\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >F2</th>        <th class=\"col_heading level0 col1\" >F3</th>        <th class=\"col_heading level0 col2\" >I2</th>        <th class=\"col_heading level0 col3\" >S1</th>        <th class=\"col_heading level0 col4\" >swe</th>        <th class=\"col_heading level0 col5\" >te</th>        <th class=\"col_heading level0 col6\" >cap</th>        <th class=\"col_heading level0 col7\" >Participant ID</th>    </tr>    <tr>        <th class=\"index_name level0\" >Sample ID</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row0\" class=\"row_heading level0 row0\" >Plate4_C2</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow0_col0\" class=\"data row0 col0\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow0_col1\" class=\"data row0 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow0_col2\" class=\"data row0 col2\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow0_col3\" class=\"data row0 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow0_col4\" class=\"data row0 col4\" >6.900</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow0_col5\" class=\"data row0 col5\" >5.400</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow0_col6\" class=\"data row0 col6\" >293.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow0_col7\" class=\"data row0 col7\" >ALD_387</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row1\" class=\"row_heading level0 row1\" >Plate2_C1</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow1_col0\" class=\"data row1 col0\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow1_col1\" class=\"data row1 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow1_col2\" class=\"data row1 col2\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow1_col3\" class=\"data row1 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow1_col4\" class=\"data row1 col4\" >5.900</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow1_col5\" class=\"data row1 col5\" >5.700</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow1_col6\" class=\"data row1 col6\" >262.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow1_col7\" class=\"data row1 col7\" >ALD_399</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row2\" class=\"row_heading level0 row2\" >Plate5_H2</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow2_col0\" class=\"data row2 col0\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow2_col1\" class=\"data row2 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow2_col2\" class=\"data row2 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow2_col3\" class=\"data row2 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow2_col4\" class=\"data row2 col4\" >6.400</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow2_col5\" class=\"data row2 col5\" >5.500</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow2_col6\" class=\"data row2 col6\" >296.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow2_col7\" class=\"data row2 col7\" >ALD_481</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row3\" class=\"row_heading level0 row3\" >Plate5_C8</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow3_col0\" class=\"data row3 col0\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow3_col1\" class=\"data row3 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow3_col2\" class=\"data row3 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow3_col3\" class=\"data row3 col3\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow3_col4\" class=\"data row3 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow3_col5\" class=\"data row3 col5\" >3.900</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow3_col6\" class=\"data row3 col6\" >264.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow3_col7\" class=\"data row3 col7\" >SIPHON_1025</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row4\" class=\"row_heading level0 row4\" >Plate4_H6</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow4_col0\" class=\"data row4 col0\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow4_col1\" class=\"data row4 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow4_col2\" class=\"data row4 col2\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow4_col3\" class=\"data row4 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow4_col4\" class=\"data row4 col4\" >5.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow4_col5\" class=\"data row4 col5\" >5.100</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow4_col6\" class=\"data row4 col6\" >277.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow4_col7\" class=\"data row4 col7\" >ALD_474</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row5\" class=\"row_heading level0 row5\" >Plate3_C9</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow5_col0\" class=\"data row5 col0\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow5_col1\" class=\"data row5 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow5_col2\" class=\"data row5 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow5_col3\" class=\"data row5 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow5_col4\" class=\"data row5 col4\" >5.900</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow5_col5\" class=\"data row5 col5\" >6.400</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow5_col6\" class=\"data row5 col6\" >304.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow5_col7\" class=\"data row5 col7\" >ALD_385</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row6\" class=\"row_heading level0 row6\" >Plate1_B10</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow6_col0\" class=\"data row6 col0\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow6_col1\" class=\"data row6 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow6_col2\" class=\"data row6 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow6_col3\" class=\"data row6 col3\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow6_col4\" class=\"data row6 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow6_col5\" class=\"data row6 col5\" >5.100</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow6_col6\" class=\"data row6 col6\" >199.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow6_col7\" class=\"data row6 col7\" >SIPHON_1236</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row7\" class=\"row_heading level0 row7\" >Plate4_D9</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow7_col0\" class=\"data row7 col0\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow7_col1\" class=\"data row7 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow7_col2\" class=\"data row7 col2\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow7_col3\" class=\"data row7 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow7_col4\" class=\"data row7 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow7_col5\" class=\"data row7 col5\" >4.200</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow7_col6\" class=\"data row7 col6\" >315.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow7_col7\" class=\"data row7 col7\" >SIPHON_1238</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row8\" class=\"row_heading level0 row8\" >Plate6_H4</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow8_col0\" class=\"data row8 col0\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow8_col1\" class=\"data row8 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow8_col2\" class=\"data row8 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow8_col3\" class=\"data row8 col3\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow8_col4\" class=\"data row8 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow8_col5\" class=\"data row8 col5\" >2.700</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow8_col6\" class=\"data row8 col6\" >230.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow8_col7\" class=\"data row8 col7\" >SIPHON_1295</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row9\" class=\"row_heading level0 row9\" >Plate4_B1</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow9_col0\" class=\"data row9 col0\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow9_col1\" class=\"data row9 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow9_col2\" class=\"data row9 col2\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow9_col3\" class=\"data row9 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow9_col4\" class=\"data row9 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow9_col5\" class=\"data row9 col5\" >1.500</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow9_col6\" class=\"data row9 col6\" >327.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow9_col7\" class=\"data row9 col7\" >SIPHON_2558</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row10\" class=\"row_heading level0 row10\" >Plate3_F1</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow10_col0\" class=\"data row10 col0\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow10_col1\" class=\"data row10 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow10_col2\" class=\"data row10 col2\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow10_col3\" class=\"data row10 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow10_col4\" class=\"data row10 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow10_col5\" class=\"data row10 col5\" >5.500</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow10_col6\" class=\"data row10 col6\" >368.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow10_col7\" class=\"data row10 col7\" >ALD_398</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row11\" class=\"row_heading level0 row11\" >Plate5_G9</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow11_col0\" class=\"data row11 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow11_col1\" class=\"data row11 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow11_col2\" class=\"data row11 col2\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow11_col3\" class=\"data row11 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow11_col4\" class=\"data row11 col4\" >5.800</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow11_col5\" class=\"data row11 col5\" >4.700</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow11_col6\" class=\"data row11 col6\" >229.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow11_col7\" class=\"data row11 col7\" >ALD_449</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row12\" class=\"row_heading level0 row12\" >Plate1_C5</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow12_col0\" class=\"data row12 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow12_col1\" class=\"data row12 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow12_col2\" class=\"data row12 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow12_col3\" class=\"data row12 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow12_col4\" class=\"data row12 col4\" >6.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow12_col5\" class=\"data row12 col5\" >5.200</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow12_col6\" class=\"data row12 col6\" >327.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow12_col7\" class=\"data row12 col7\" >ALD_465</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row13\" class=\"row_heading level0 row13\" >Plate3_C11</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow13_col0\" class=\"data row13 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow13_col1\" class=\"data row13 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow13_col2\" class=\"data row13 col2\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow13_col3\" class=\"data row13 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow13_col4\" class=\"data row13 col4\" >7.100</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow13_col5\" class=\"data row13 col5\" >5.100</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow13_col6\" class=\"data row13 col6\" >368.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow13_col7\" class=\"data row13 col7\" >ALD_393</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row14\" class=\"row_heading level0 row14\" >Plate6_A9</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow14_col0\" class=\"data row14 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow14_col1\" class=\"data row14 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow14_col2\" class=\"data row14 col2\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow14_col3\" class=\"data row14 col3\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow14_col4\" class=\"data row14 col4\" >4.200</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow14_col5\" class=\"data row14 col5\" >4.500</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow14_col6\" class=\"data row14 col6\" >280.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow14_col7\" class=\"data row14 col7\" >ALD_405</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row15\" class=\"row_heading level0 row15\" >Plate5_H5</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow15_col0\" class=\"data row15 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow15_col1\" class=\"data row15 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow15_col2\" class=\"data row15 col2\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow15_col3\" class=\"data row15 col3\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow15_col4\" class=\"data row15 col4\" >6.100</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow15_col5\" class=\"data row15 col5\" >5.100</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow15_col6\" class=\"data row15 col6\" >236.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow15_col7\" class=\"data row15 col7\" >ALD_438</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row16\" class=\"row_heading level0 row16\" >Plate4_C9</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow16_col0\" class=\"data row16 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow16_col1\" class=\"data row16 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow16_col2\" class=\"data row16 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow16_col3\" class=\"data row16 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow16_col4\" class=\"data row16 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow16_col5\" class=\"data row16 col5\" >4.200</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow16_col6\" class=\"data row16 col6\" >295.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow16_col7\" class=\"data row16 col7\" >SIPHON_1228</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row17\" class=\"row_heading level0 row17\" >Plate4_F6</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow17_col0\" class=\"data row17 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow17_col1\" class=\"data row17 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow17_col2\" class=\"data row17 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow17_col3\" class=\"data row17 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow17_col4\" class=\"data row17 col4\" >6.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow17_col5\" class=\"data row17 col5\" >4.800</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow17_col6\" class=\"data row17 col6\" >292.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow17_col7\" class=\"data row17 col7\" >ALD_424</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row18\" class=\"row_heading level0 row18\" >Plate1_C11</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow18_col0\" class=\"data row18 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow18_col1\" class=\"data row18 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow18_col2\" class=\"data row18 col2\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow18_col3\" class=\"data row18 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow18_col4\" class=\"data row18 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow18_col5\" class=\"data row18 col5\" >5.100</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow18_col6\" class=\"data row18 col6\" >280.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow18_col7\" class=\"data row18 col7\" >ALD_483</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row19\" class=\"row_heading level0 row19\" >Plate2_C6</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow19_col0\" class=\"data row19 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow19_col1\" class=\"data row19 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow19_col2\" class=\"data row19 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow19_col3\" class=\"data row19 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow19_col4\" class=\"data row19 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow19_col5\" class=\"data row19 col5\" >3.500</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow19_col6\" class=\"data row19 col6\" >333.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow19_col7\" class=\"data row19 col7\" >SIPHON_2265</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row20\" class=\"row_heading level0 row20\" >Plate4_H3</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow20_col0\" class=\"data row20 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow20_col1\" class=\"data row20 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow20_col2\" class=\"data row20 col2\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow20_col3\" class=\"data row20 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow20_col4\" class=\"data row20 col4\" >4.300</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow20_col5\" class=\"data row20 col5\" >4.300</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow20_col6\" class=\"data row20 col6\" >332.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow20_col7\" class=\"data row20 col7\" >ALD_471</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row21\" class=\"row_heading level0 row21\" >Plate5_E4</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow21_col0\" class=\"data row21 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow21_col1\" class=\"data row21 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow21_col2\" class=\"data row21 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow21_col3\" class=\"data row21 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow21_col4\" class=\"data row21 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow21_col5\" class=\"data row21 col5\" >5.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow21_col6\" class=\"data row21 col6\" >343.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow21_col7\" class=\"data row21 col7\" >SIPHON_2573</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row22\" class=\"row_heading level0 row22\" >Plate6_G5</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow22_col0\" class=\"data row22 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow22_col1\" class=\"data row22 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow22_col2\" class=\"data row22 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow22_col3\" class=\"data row22 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow22_col4\" class=\"data row22 col4\" >7.800</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow22_col5\" class=\"data row22 col5\" >4.400</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow22_col6\" class=\"data row22 col6\" >163.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow22_col7\" class=\"data row22 col7\" >ALD_414</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row23\" class=\"row_heading level0 row23\" >Plate2_F1</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow23_col0\" class=\"data row23 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow23_col1\" class=\"data row23 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow23_col2\" class=\"data row23 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow23_col3\" class=\"data row23 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow23_col4\" class=\"data row23 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow23_col5\" class=\"data row23 col5\" >4.800</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow23_col6\" class=\"data row23 col6\" >293.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow23_col7\" class=\"data row23 col7\" >SIPHON_1893</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row24\" class=\"row_heading level0 row24\" >Plate4_H8</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow24_col0\" class=\"data row24 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow24_col1\" class=\"data row24 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow24_col2\" class=\"data row24 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow24_col3\" class=\"data row24 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow24_col4\" class=\"data row24 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow24_col5\" class=\"data row24 col5\" >3.900</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow24_col6\" class=\"data row24 col6\" >158.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow24_col7\" class=\"data row24 col7\" >SIPHON_1655</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row25\" class=\"row_heading level0 row25\" >Plate4_A3</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow25_col0\" class=\"data row25 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow25_col1\" class=\"data row25 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow25_col2\" class=\"data row25 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow25_col3\" class=\"data row25 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow25_col4\" class=\"data row25 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow25_col5\" class=\"data row25 col5\" >5.100</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow25_col6\" class=\"data row25 col6\" >295.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow25_col7\" class=\"data row25 col7\" >SIPHON_1134</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row26\" class=\"row_heading level0 row26\" >Plate6_B6</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow26_col0\" class=\"data row26 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow26_col1\" class=\"data row26 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow26_col2\" class=\"data row26 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow26_col3\" class=\"data row26 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow26_col4\" class=\"data row26 col4\" >5.700</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow26_col5\" class=\"data row26 col5\" >5.800</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow26_col6\" class=\"data row26 col6\" >243.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow26_col7\" class=\"data row26 col7\" >ALD_439</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row27\" class=\"row_heading level0 row27\" >Plate3_H3</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow27_col0\" class=\"data row27 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow27_col1\" class=\"data row27 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow27_col2\" class=\"data row27 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow27_col3\" class=\"data row27 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow27_col4\" class=\"data row27 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow27_col5\" class=\"data row27 col5\" >3.900</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow27_col6\" class=\"data row27 col6\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow27_col7\" class=\"data row27 col7\" >SIPHON_1641</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row28\" class=\"row_heading level0 row28\" >Plate2_D12</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow28_col0\" class=\"data row28 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow28_col1\" class=\"data row28 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow28_col2\" class=\"data row28 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow28_col3\" class=\"data row28 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow28_col4\" class=\"data row28 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow28_col5\" class=\"data row28 col5\" >4.400</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow28_col6\" class=\"data row28 col6\" >324.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow28_col7\" class=\"data row28 col7\" >SIPHON_2001</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row29\" class=\"row_heading level0 row29\" >Plate7_A5</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow29_col0\" class=\"data row29 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow29_col1\" class=\"data row29 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow29_col2\" class=\"data row29 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow29_col3\" class=\"data row29 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow29_col4\" class=\"data row29 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow29_col5\" class=\"data row29 col5\" >5.800</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow29_col6\" class=\"data row29 col6\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow29_col7\" class=\"data row29 col7\" >SIPHON_1106</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row30\" class=\"row_heading level0 row30\" >Plate3_C3</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow30_col0\" class=\"data row30 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow30_col1\" class=\"data row30 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow30_col2\" class=\"data row30 col2\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow30_col3\" class=\"data row30 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow30_col4\" class=\"data row30 col4\" >5.800</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow30_col5\" class=\"data row30 col5\" >4.800</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow30_col6\" class=\"data row30 col6\" >324.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow30_col7\" class=\"data row30 col7\" >ALD_425</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row31\" class=\"row_heading level0 row31\" >Plate1_C7</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow31_col0\" class=\"data row31 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow31_col1\" class=\"data row31 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow31_col2\" class=\"data row31 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow31_col3\" class=\"data row31 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow31_col4\" class=\"data row31 col4\" >7.200</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow31_col5\" class=\"data row31 col5\" >4.800</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow31_col6\" class=\"data row31 col6\" >364.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow31_col7\" class=\"data row31 col7\" >ALD_416</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row32\" class=\"row_heading level0 row32\" >Plate2_C2</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow32_col0\" class=\"data row32 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow32_col1\" class=\"data row32 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow32_col2\" class=\"data row32 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow32_col3\" class=\"data row32 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow32_col4\" class=\"data row32 col4\" >5.700</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow32_col5\" class=\"data row32 col5\" >5.300</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow32_col6\" class=\"data row32 col6\" >393.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow32_col7\" class=\"data row32 col7\" >ALD_480</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row33\" class=\"row_heading level0 row33\" >Plate2_D9</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow33_col0\" class=\"data row33 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow33_col1\" class=\"data row33 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow33_col2\" class=\"data row33 col2\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow33_col3\" class=\"data row33 col3\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow33_col4\" class=\"data row33 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow33_col5\" class=\"data row33 col5\" >4.900</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow33_col6\" class=\"data row33 col6\" >327.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow33_col7\" class=\"data row33 col7\" >SIPHON_1310</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row34\" class=\"row_heading level0 row34\" >Plate5_D10</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow34_col0\" class=\"data row34 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow34_col1\" class=\"data row34 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow34_col2\" class=\"data row34 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow34_col3\" class=\"data row34 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow34_col4\" class=\"data row34 col4\" >7.300</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow34_col5\" class=\"data row34 col5\" >3.500</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow34_col6\" class=\"data row34 col6\" >320.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow34_col7\" class=\"data row34 col7\" >ALD_266</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row35\" class=\"row_heading level0 row35\" >Plate5_D9</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow35_col0\" class=\"data row35 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow35_col1\" class=\"data row35 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow35_col2\" class=\"data row35 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow35_col3\" class=\"data row35 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow35_col4\" class=\"data row35 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow35_col5\" class=\"data row35 col5\" >5.400</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow35_col6\" class=\"data row35 col6\" >224.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow35_col7\" class=\"data row35 col7\" >SIPHON_2576</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row36\" class=\"row_heading level0 row36\" >Plate7_A10</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow36_col0\" class=\"data row36 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow36_col1\" class=\"data row36 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow36_col2\" class=\"data row36 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow36_col3\" class=\"data row36 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow36_col4\" class=\"data row36 col4\" >7.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow36_col5\" class=\"data row36 col5\" >5.300</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow36_col6\" class=\"data row36 col6\" >228.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow36_col7\" class=\"data row36 col7\" >ALD_365</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row37\" class=\"row_heading level0 row37\" >Plate4_B11</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow37_col0\" class=\"data row37 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow37_col1\" class=\"data row37 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow37_col2\" class=\"data row37 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow37_col3\" class=\"data row37 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow37_col4\" class=\"data row37 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow37_col5\" class=\"data row37 col5\" >4.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow37_col6\" class=\"data row37 col6\" >304.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow37_col7\" class=\"data row37 col7\" >SIPHON_1170</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row38\" class=\"row_heading level0 row38\" >Plate4_B5</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow38_col0\" class=\"data row38 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow38_col1\" class=\"data row38 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow38_col2\" class=\"data row38 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow38_col3\" class=\"data row38 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow38_col4\" class=\"data row38 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow38_col5\" class=\"data row38 col5\" >4.900</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow38_col6\" class=\"data row38 col6\" >281.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow38_col7\" class=\"data row38 col7\" >SIPHON_1217</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row39\" class=\"row_heading level0 row39\" >Plate3_C1</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow39_col0\" class=\"data row39 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow39_col1\" class=\"data row39 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow39_col2\" class=\"data row39 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow39_col3\" class=\"data row39 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow39_col4\" class=\"data row39 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow39_col5\" class=\"data row39 col5\" >3.900</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow39_col6\" class=\"data row39 col6\" >207.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow39_col7\" class=\"data row39 col7\" >SIPHON_1620</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row40\" class=\"row_heading level0 row40\" >Plate5_B9</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow40_col0\" class=\"data row40 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow40_col1\" class=\"data row40 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow40_col2\" class=\"data row40 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow40_col3\" class=\"data row40 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow40_col4\" class=\"data row40 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow40_col5\" class=\"data row40 col5\" >3.700</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow40_col6\" class=\"data row40 col6\" >291.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow40_col7\" class=\"data row40 col7\" >SIPHON_2541</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row41\" class=\"row_heading level0 row41\" >Plate5_H3</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow41_col0\" class=\"data row41 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow41_col1\" class=\"data row41 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow41_col2\" class=\"data row41 col2\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow41_col3\" class=\"data row41 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow41_col4\" class=\"data row41 col4\" >6.200</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow41_col5\" class=\"data row41 col5\" >5.900</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow41_col6\" class=\"data row41 col6\" >379.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow41_col7\" class=\"data row41 col7\" >ALD_377</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row42\" class=\"row_heading level0 row42\" >Plate6_C12</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow42_col0\" class=\"data row42 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow42_col1\" class=\"data row42 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow42_col2\" class=\"data row42 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow42_col3\" class=\"data row42 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow42_col4\" class=\"data row42 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow42_col5\" class=\"data row42 col5\" >5.100</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow42_col6\" class=\"data row42 col6\" >241.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow42_col7\" class=\"data row42 col7\" >SIPHON_1623</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row43\" class=\"row_heading level0 row43\" >Plate6_F5</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow43_col0\" class=\"data row43 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow43_col1\" class=\"data row43 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow43_col2\" class=\"data row43 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow43_col3\" class=\"data row43 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow43_col4\" class=\"data row43 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow43_col5\" class=\"data row43 col5\" >4.600</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow43_col6\" class=\"data row43 col6\" >355.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow43_col7\" class=\"data row43 col7\" >SIPHON_2547</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row44\" class=\"row_heading level0 row44\" >Plate4_E7</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow44_col0\" class=\"data row44 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow44_col1\" class=\"data row44 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow44_col2\" class=\"data row44 col2\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow44_col3\" class=\"data row44 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow44_col4\" class=\"data row44 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow44_col5\" class=\"data row44 col5\" >5.600</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow44_col6\" class=\"data row44 col6\" >295.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow44_col7\" class=\"data row44 col7\" >SIPHON_1683</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row45\" class=\"row_heading level0 row45\" >Plate6_H6</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow45_col0\" class=\"data row45 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow45_col1\" class=\"data row45 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow45_col2\" class=\"data row45 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow45_col3\" class=\"data row45 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow45_col4\" class=\"data row45 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow45_col5\" class=\"data row45 col5\" >4.600</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow45_col6\" class=\"data row45 col6\" >280.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow45_col7\" class=\"data row45 col7\" >SIPHON_1645</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row46\" class=\"row_heading level0 row46\" >Plate5_C2</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow46_col0\" class=\"data row46 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow46_col1\" class=\"data row46 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow46_col2\" class=\"data row46 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow46_col3\" class=\"data row46 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow46_col4\" class=\"data row46 col4\" >7.300</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow46_col5\" class=\"data row46 col5\" >5.400</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow46_col6\" class=\"data row46 col6\" >229.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow46_col7\" class=\"data row46 col7\" >ALD_413</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row47\" class=\"row_heading level0 row47\" >Plate3_F3</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow47_col0\" class=\"data row47 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow47_col1\" class=\"data row47 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow47_col2\" class=\"data row47 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow47_col3\" class=\"data row47 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow47_col4\" class=\"data row47 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow47_col5\" class=\"data row47 col5\" >5.600</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow47_col6\" class=\"data row47 col6\" >376.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow47_col7\" class=\"data row47 col7\" >SIPHON_1298</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row48\" class=\"row_heading level0 row48\" >Plate5_A10</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow48_col0\" class=\"data row48 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow48_col1\" class=\"data row48 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow48_col2\" class=\"data row48 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow48_col3\" class=\"data row48 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow48_col4\" class=\"data row48 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow48_col5\" class=\"data row48 col5\" >4.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow48_col6\" class=\"data row48 col6\" >236.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow48_col7\" class=\"data row48 col7\" >SIPHON_1104</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row49\" class=\"row_heading level0 row49\" >Plate1_B11</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow49_col0\" class=\"data row49 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow49_col1\" class=\"data row49 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow49_col2\" class=\"data row49 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow49_col3\" class=\"data row49 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow49_col4\" class=\"data row49 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow49_col5\" class=\"data row49 col5\" >4.300</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow49_col6\" class=\"data row49 col6\" >315.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow49_col7\" class=\"data row49 col7\" >ALD_478</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row50\" class=\"row_heading level0 row50\" >Plate2_F2</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow50_col0\" class=\"data row50 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow50_col1\" class=\"data row50 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow50_col2\" class=\"data row50 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow50_col3\" class=\"data row50 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow50_col4\" class=\"data row50 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow50_col5\" class=\"data row50 col5\" >4.500</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow50_col6\" class=\"data row50 col6\" >339.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow50_col7\" class=\"data row50 col7\" >SIPHON_1432</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43blevel0_row51\" class=\"row_heading level0 row51\" >Plate2_D7</th>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow51_col0\" class=\"data row51 col0\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow51_col1\" class=\"data row51 col1\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow51_col2\" class=\"data row51 col2\" >0</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow51_col3\" class=\"data row51 col3\" >1</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow51_col4\" class=\"data row51 col4\" >nan</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow51_col5\" class=\"data row51 col5\" >4.100</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow51_col6\" class=\"data row51 col6\" >213.000</td>\n",
+       "                        <td id=\"T_df95da6e_2dfd_11ec_b7fa_367ddaa5d43brow51_col7\" class=\"data row51 col7\" >SIPHON_1026</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f9f40d0d710>"
+      ]
+     },
+     "execution_count": 323,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "at_risk_pos = at_risk_pred_pos_all.join(data_cli_atrisk[['swe', 'te', 'cap']], how='left')\n",
     "if not doubleID.empty:\n",
@@ -3994,7 +16245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 324,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4003,9 +16254,392 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 325,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow0_col0,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow0_col2,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow1_col0,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow2_col0,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow3_col0,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow4_col0,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow5_col0,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow6_col2,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow7_col3,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow8_col3,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow9_col3,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow10_col3,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow11_col2,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow11_col3,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow12_col3,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow13_col3,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow14_col3,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow15_col3,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow16_col3,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow17_col2,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow18_col3,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow19_col2,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow20_col3,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow21_col3,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow22_col3,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow23_col3,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow24_col3,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow25_col3,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow26_col3,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow27_col3,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow28_col3,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow29_col3,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow30_col3,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow31_col3,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow32_col3{\n",
+       "            background:  yellow;\n",
+       "        }#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow0_col6,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow7_col6,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow9_col6,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow11_col6,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow12_col6,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow15_col6,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow16_col6,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow21_col6,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow23_col6,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow26_col6,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow27_col6,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow28_col6,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow30_col6,#T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow31_col6{\n",
+       "            background:  pink;\n",
+       "        }</style><table id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43b\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >F2</th>        <th class=\"col_heading level0 col1\" >F3</th>        <th class=\"col_heading level0 col2\" >I2</th>        <th class=\"col_heading level0 col3\" >S1</th>        <th class=\"col_heading level0 col4\" >swe</th>        <th class=\"col_heading level0 col5\" >te</th>        <th class=\"col_heading level0 col6\" >cap</th>        <th class=\"col_heading level0 col7\" >Participant ID</th>    </tr>    <tr>        <th class=\"index_name level0\" >Sample ID</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row0\" class=\"row_heading level0 row0\" >Plate5_G11</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow0_col0\" class=\"data row0 col0\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow0_col1\" class=\"data row0 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow0_col2\" class=\"data row0 col2\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow0_col3\" class=\"data row0 col3\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow0_col4\" class=\"data row0 col4\" >6.200</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow0_col5\" class=\"data row0 col5\" >5.300</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow0_col6\" class=\"data row0 col6\" >292.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow0_col7\" class=\"data row0 col7\" >HP_182</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row1\" class=\"row_heading level0 row1\" >Plate1_D7</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow1_col0\" class=\"data row1 col0\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow1_col1\" class=\"data row1 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow1_col2\" class=\"data row1 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow1_col3\" class=\"data row1 col3\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow1_col4\" class=\"data row1 col4\" >6.800</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow1_col5\" class=\"data row1 col5\" >4.800</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow1_col6\" class=\"data row1 col6\" >220.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow1_col7\" class=\"data row1 col7\" >HP_386</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row2\" class=\"row_heading level0 row2\" >Plate1_C12</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow2_col0\" class=\"data row2 col0\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow2_col1\" class=\"data row2 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow2_col2\" class=\"data row2 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow2_col3\" class=\"data row2 col3\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow2_col4\" class=\"data row2 col4\" >6.500</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow2_col5\" class=\"data row2 col5\" >3.400</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow2_col6\" class=\"data row2 col6\" >112.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow2_col7\" class=\"data row2 col7\" >HP_292</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row3\" class=\"row_heading level0 row3\" >Plate3_G12</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow3_col0\" class=\"data row3 col0\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow3_col1\" class=\"data row3 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow3_col2\" class=\"data row3 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow3_col3\" class=\"data row3 col3\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow3_col4\" class=\"data row3 col4\" >6.200</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow3_col5\" class=\"data row3 col5\" >5.100</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow3_col6\" class=\"data row3 col6\" >204.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow3_col7\" class=\"data row3 col7\" >HP_218</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row4\" class=\"row_heading level0 row4\" >Plate4_G10</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow4_col0\" class=\"data row4 col0\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow4_col1\" class=\"data row4 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow4_col2\" class=\"data row4 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow4_col3\" class=\"data row4 col3\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow4_col4\" class=\"data row4 col4\" >7.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow4_col5\" class=\"data row4 col5\" >7.900</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow4_col6\" class=\"data row4 col6\" >166.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow4_col7\" class=\"data row4 col7\" >HP_4</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row5\" class=\"row_heading level0 row5\" >Plate1_H10</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow5_col0\" class=\"data row5 col0\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow5_col1\" class=\"data row5 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow5_col2\" class=\"data row5 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow5_col3\" class=\"data row5 col3\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow5_col4\" class=\"data row5 col4\" >4.900</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow5_col5\" class=\"data row5 col5\" >5.300</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow5_col6\" class=\"data row5 col6\" >289.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow5_col7\" class=\"data row5 col7\" >HP_572</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row6\" class=\"row_heading level0 row6\" >Plate1_B1</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow6_col0\" class=\"data row6 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow6_col1\" class=\"data row6 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow6_col2\" class=\"data row6 col2\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow6_col3\" class=\"data row6 col3\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow6_col4\" class=\"data row6 col4\" >5.300</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow6_col5\" class=\"data row6 col5\" >4.800</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow6_col6\" class=\"data row6 col6\" >266.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow6_col7\" class=\"data row6 col7\" >HP_485</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row7\" class=\"row_heading level0 row7\" >Plate1_H1</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow7_col0\" class=\"data row7 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow7_col1\" class=\"data row7 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow7_col2\" class=\"data row7 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow7_col3\" class=\"data row7 col3\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow7_col4\" class=\"data row7 col4\" >8.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow7_col5\" class=\"data row7 col5\" >4.300</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow7_col6\" class=\"data row7 col6\" >334.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow7_col7\" class=\"data row7 col7\" >HP_350</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row8\" class=\"row_heading level0 row8\" >Plate4_A12</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow8_col0\" class=\"data row8 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow8_col1\" class=\"data row8 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow8_col2\" class=\"data row8 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow8_col3\" class=\"data row8 col3\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow8_col4\" class=\"data row8 col4\" >4.600</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow8_col5\" class=\"data row8 col5\" >3.700</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow8_col6\" class=\"data row8 col6\" >247.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow8_col7\" class=\"data row8 col7\" >HP_551</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row9\" class=\"row_heading level0 row9\" >Plate4_C1</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow9_col0\" class=\"data row9 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow9_col1\" class=\"data row9 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow9_col2\" class=\"data row9 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow9_col3\" class=\"data row9 col3\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow9_col4\" class=\"data row9 col4\" >6.600</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow9_col5\" class=\"data row9 col5\" >5.300</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow9_col6\" class=\"data row9 col6\" >341.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow9_col7\" class=\"data row9 col7\" >HP_666</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row10\" class=\"row_heading level0 row10\" >Plate3_G11</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow10_col0\" class=\"data row10 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow10_col1\" class=\"data row10 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow10_col2\" class=\"data row10 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow10_col3\" class=\"data row10 col3\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow10_col4\" class=\"data row10 col4\" >4.900</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow10_col5\" class=\"data row10 col5\" >5.900</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow10_col6\" class=\"data row10 col6\" >250.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow10_col7\" class=\"data row10 col7\" >HP_746</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row11\" class=\"row_heading level0 row11\" >Plate1_G4</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow11_col0\" class=\"data row11 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow11_col1\" class=\"data row11 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow11_col2\" class=\"data row11 col2\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow11_col3\" class=\"data row11 col3\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow11_col4\" class=\"data row11 col4\" >5.300</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow11_col5\" class=\"data row11 col5\" >6.100</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow11_col6\" class=\"data row11 col6\" >297.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow11_col7\" class=\"data row11 col7\" >HP_49</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row12\" class=\"row_heading level0 row12\" >Plate2_F4</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow12_col0\" class=\"data row12 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow12_col1\" class=\"data row12 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow12_col2\" class=\"data row12 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow12_col3\" class=\"data row12 col3\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow12_col4\" class=\"data row12 col4\" >5.500</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow12_col5\" class=\"data row12 col5\" >6.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow12_col6\" class=\"data row12 col6\" >298.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow12_col7\" class=\"data row12 col7\" >HP_384</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row13\" class=\"row_heading level0 row13\" >Plate3_D7</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow13_col0\" class=\"data row13 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow13_col1\" class=\"data row13 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow13_col2\" class=\"data row13 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow13_col3\" class=\"data row13 col3\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow13_col4\" class=\"data row13 col4\" >5.600</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow13_col5\" class=\"data row13 col5\" >6.100</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow13_col6\" class=\"data row13 col6\" >261.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow13_col7\" class=\"data row13 col7\" >HP_400</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row14\" class=\"row_heading level0 row14\" >Plate6_E2</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow14_col0\" class=\"data row14 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow14_col1\" class=\"data row14 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow14_col2\" class=\"data row14 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow14_col3\" class=\"data row14 col3\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow14_col4\" class=\"data row14 col4\" >4.700</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow14_col5\" class=\"data row14 col5\" >3.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow14_col6\" class=\"data row14 col6\" >183.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow14_col7\" class=\"data row14 col7\" >HP_315</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row15\" class=\"row_heading level0 row15\" >Plate3_C2</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow15_col0\" class=\"data row15 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow15_col1\" class=\"data row15 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow15_col2\" class=\"data row15 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow15_col3\" class=\"data row15 col3\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow15_col4\" class=\"data row15 col4\" >7.100</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow15_col5\" class=\"data row15 col5\" >4.100</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow15_col6\" class=\"data row15 col6\" >379.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow15_col7\" class=\"data row15 col7\" >HP_197</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row16\" class=\"row_heading level0 row16\" >Plate4_H10</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow16_col0\" class=\"data row16 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow16_col1\" class=\"data row16 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow16_col2\" class=\"data row16 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow16_col3\" class=\"data row16 col3\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow16_col4\" class=\"data row16 col4\" >6.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow16_col5\" class=\"data row16 col5\" >4.900</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow16_col6\" class=\"data row16 col6\" >351.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow16_col7\" class=\"data row16 col7\" >HP_209</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row17\" class=\"row_heading level0 row17\" >Plate4_D11</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow17_col0\" class=\"data row17 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow17_col1\" class=\"data row17 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow17_col2\" class=\"data row17 col2\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow17_col3\" class=\"data row17 col3\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow17_col4\" class=\"data row17 col4\" >4.800</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow17_col5\" class=\"data row17 col5\" >3.300</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow17_col6\" class=\"data row17 col6\" >249.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow17_col7\" class=\"data row17 col7\" >HP_273</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row18\" class=\"row_heading level0 row18\" >Plate5_F7</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow18_col0\" class=\"data row18 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow18_col1\" class=\"data row18 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow18_col2\" class=\"data row18 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow18_col3\" class=\"data row18 col3\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow18_col4\" class=\"data row18 col4\" >5.700</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow18_col5\" class=\"data row18 col5\" >5.700</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow18_col6\" class=\"data row18 col6\" >248.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow18_col7\" class=\"data row18 col7\" >HP_175</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row19\" class=\"row_heading level0 row19\" >Plate2_D4</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow19_col0\" class=\"data row19 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow19_col1\" class=\"data row19 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow19_col2\" class=\"data row19 col2\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow19_col3\" class=\"data row19 col3\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow19_col4\" class=\"data row19 col4\" >7.200</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow19_col5\" class=\"data row19 col5\" >5.900</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow19_col6\" class=\"data row19 col6\" >245.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow19_col7\" class=\"data row19 col7\" >HP_260</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row20\" class=\"row_heading level0 row20\" >Plate6_B5</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow20_col0\" class=\"data row20 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow20_col1\" class=\"data row20 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow20_col2\" class=\"data row20 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow20_col3\" class=\"data row20 col3\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow20_col4\" class=\"data row20 col4\" >5.200</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow20_col5\" class=\"data row20 col5\" >3.800</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow20_col6\" class=\"data row20 col6\" >281.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow20_col7\" class=\"data row20 col7\" >HP_653</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row21\" class=\"row_heading level0 row21\" >Plate4_H1</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow21_col0\" class=\"data row21 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow21_col1\" class=\"data row21 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow21_col2\" class=\"data row21 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow21_col3\" class=\"data row21 col3\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow21_col4\" class=\"data row21 col4\" >5.800</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow21_col5\" class=\"data row21 col5\" >5.600</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow21_col6\" class=\"data row21 col6\" >305.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow21_col7\" class=\"data row21 col7\" >HP_377</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row22\" class=\"row_heading level0 row22\" >Plate3_C8</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow22_col0\" class=\"data row22 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow22_col1\" class=\"data row22 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow22_col2\" class=\"data row22 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow22_col3\" class=\"data row22 col3\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow22_col4\" class=\"data row22 col4\" >5.200</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow22_col5\" class=\"data row22 col5\" >5.400</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow22_col6\" class=\"data row22 col6\" >235.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow22_col7\" class=\"data row22 col7\" >HP_737</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row23\" class=\"row_heading level0 row23\" >Plate3_B1</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow23_col0\" class=\"data row23 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow23_col1\" class=\"data row23 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow23_col2\" class=\"data row23 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow23_col3\" class=\"data row23 col3\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow23_col4\" class=\"data row23 col4\" >6.600</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow23_col5\" class=\"data row23 col5\" >5.400</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow23_col6\" class=\"data row23 col6\" >325.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow23_col7\" class=\"data row23 col7\" >HP_139</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row24\" class=\"row_heading level0 row24\" >Plate3_F2</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow24_col0\" class=\"data row24 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow24_col1\" class=\"data row24 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow24_col2\" class=\"data row24 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow24_col3\" class=\"data row24 col3\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow24_col4\" class=\"data row24 col4\" >4.500</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow24_col5\" class=\"data row24 col5\" >4.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow24_col6\" class=\"data row24 col6\" >178.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow24_col7\" class=\"data row24 col7\" >HP_289</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row25\" class=\"row_heading level0 row25\" >Plate5_G3</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow25_col0\" class=\"data row25 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow25_col1\" class=\"data row25 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow25_col2\" class=\"data row25 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow25_col3\" class=\"data row25 col3\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow25_col4\" class=\"data row25 col4\" >4.800</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow25_col5\" class=\"data row25 col5\" >5.400</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow25_col6\" class=\"data row25 col6\" >268.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow25_col7\" class=\"data row25 col7\" >HP_720</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row26\" class=\"row_heading level0 row26\" >Plate6_B11</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow26_col0\" class=\"data row26 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow26_col1\" class=\"data row26 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow26_col2\" class=\"data row26 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow26_col3\" class=\"data row26 col3\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow26_col4\" class=\"data row26 col4\" >6.300</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow26_col5\" class=\"data row26 col5\" >4.300</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow26_col6\" class=\"data row26 col6\" >336.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow26_col7\" class=\"data row26 col7\" >HP_322</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row27\" class=\"row_heading level0 row27\" >Plate2_A10</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow27_col0\" class=\"data row27 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow27_col1\" class=\"data row27 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow27_col2\" class=\"data row27 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow27_col3\" class=\"data row27 col3\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow27_col4\" class=\"data row27 col4\" >5.700</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow27_col5\" class=\"data row27 col5\" >5.200</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow27_col6\" class=\"data row27 col6\" >389.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow27_col7\" class=\"data row27 col7\" >HP_112</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row28\" class=\"row_heading level0 row28\" >Plate1_F11</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow28_col0\" class=\"data row28 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow28_col1\" class=\"data row28 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow28_col2\" class=\"data row28 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow28_col3\" class=\"data row28 col3\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow28_col4\" class=\"data row28 col4\" >7.100</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow28_col5\" class=\"data row28 col5\" >7.400</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow28_col6\" class=\"data row28 col6\" >379.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow28_col7\" class=\"data row28 col7\" >HP_596</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row29\" class=\"row_heading level0 row29\" >Plate2_C12</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow29_col0\" class=\"data row29 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow29_col1\" class=\"data row29 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow29_col2\" class=\"data row29 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow29_col3\" class=\"data row29 col3\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow29_col4\" class=\"data row29 col4\" >5.800</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow29_col5\" class=\"data row29 col5\" >4.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow29_col6\" class=\"data row29 col6\" >189.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow29_col7\" class=\"data row29 col7\" >HP_642</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row30\" class=\"row_heading level0 row30\" >Plate4_C7</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow30_col0\" class=\"data row30 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow30_col1\" class=\"data row30 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow30_col2\" class=\"data row30 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow30_col3\" class=\"data row30 col3\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow30_col4\" class=\"data row30 col4\" >5.500</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow30_col5\" class=\"data row30 col5\" >4.300</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow30_col6\" class=\"data row30 col6\" >303.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow30_col7\" class=\"data row30 col7\" >HP_335</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row31\" class=\"row_heading level0 row31\" >Plate6_H5</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow31_col0\" class=\"data row31 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow31_col1\" class=\"data row31 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow31_col2\" class=\"data row31 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow31_col3\" class=\"data row31 col3\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow31_col4\" class=\"data row31 col4\" >3.900</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow31_col5\" class=\"data row31 col5\" >3.500</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow31_col6\" class=\"data row31 col6\" >292.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow31_col7\" class=\"data row31 col7\" >HP_374</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43blevel0_row32\" class=\"row_heading level0 row32\" >Plate6_H11</th>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow32_col0\" class=\"data row32 col0\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow32_col1\" class=\"data row32 col1\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow32_col2\" class=\"data row32 col2\" >0</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow32_col3\" class=\"data row32 col3\" >1</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow32_col4\" class=\"data row32 col4\" >4.100</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow32_col5\" class=\"data row32 col5\" >3.200</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow32_col6\" class=\"data row32 col6\" >244.000</td>\n",
+       "                        <td id=\"T_df9dd638_2dfd_11ec_b7fa_367ddaa5d43brow32_col7\" class=\"data row32 col7\" >HP_8</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f9f018bab50>"
+      ]
+     },
+     "execution_count": 325,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "healthy_pos = healthy_pred_pos_all.join(data_cli[['swe', 'te', 'cap']], how='left')\n",
     "if not doubleID.empty:\n",
@@ -4030,7 +16664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 326,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4040,7 +16674,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 327,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4057,9 +16691,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 328,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, axes = plt.subplots(ncols=3,figsize=(12,4))\n",
     "for i, endpoint in enumerate(['F2', 'I2', 'S1']):\n",
@@ -4069,9 +16716,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 329,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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mvL0NNhZWMjCte7sjrRmamYjdJny7rTQg/RkMfYlAVW8XEXlIRDaLyBoR6flPvZ/UNbrZXV7PgPSeT0cAopx2hmQmGMEwGNrAnxFGc/X2yUAeMEdEZrZqcxww0jouBR4LpJEdsanIWr/opv9FWwzvl8yWomqqG1wB69Ng6At0KhhK01n19pOB56y2y4EUEckJrKlts6mwecEzcIIxsl8yCoybuMHQikBVbx8AFLR4vtM617qfgFdv37S7itSEaOJj2s4O3h0GZcQT7bCZaYnB0IpAVW9vKzz0J9svwajevqmoigGpcQHpqxm7zcbQ7CS+3WoEw2BoSUCqt6NHFINaPB8I7O6JYf7Q6PZSWF5PvwALBsDInGR2V9RTUtUQ8L4NkUVHldvvuOMOFi5c2OH1d911F3/961+7fN9wDIfv1DVcRDIBt1KqskX19vtbNXsbuFpEXgYOBKqUUoUBt7YVO/bUoICclMALRnMt1m+3lXJs3qBOWhv2V1oHlgWK5nD4cCNQ1dvfB7YCm4EngSuDYm0r8vfoqlHBGGH0S4klIcbJKrOOYaDtyu0Ac+fO5T//+Q8A77//PmPGjGHWrFlce+21nHDCCXuv//7775k9ezbDhg3joYceavMekRAOH6jq7Qq4KrCmdc62khqcdhvpCYEP0hERhvdL4tttZSilEOlaFi9DkLj+emgnnLzb5OXBP/7RYZO2Krefd955e19vbGzksssu47PPPmPo0KGcffbZ+1y/YcMGFi1aRE1NDaNHj+aKK67A6dx3oT4SwuEj2tMzv7iG7JRYbF1MyecvI/slU1HXxPY9tZ03NvRp2qrc3pINGzYwbNgwhg7VUc6tBePnP/850dHRZGRkkJWVRXFx8U/uEQnh8BEd3r6tpIZh2V1L+NsVRuTo/5hV+aXkZgXvPoYu0MlIIFi0rtzePCVpprOYrNbXtxWJGgnh8BE7wqiqd1FR1xSU9Ytm0hJiSE+M5tttxoHL0DFjxoxh69ate0cer7zySsD6Dqdw+IgdYWwrqQaCs0PSkuHZyazJL8Pr82EPcV1LQ/gSGxvLo48+ypw5c8jIyGDGjBkB63vXrl1cdNFFe0Pd2wqHr6qq4vzzz+eFF14Iav3ViA1vf+urbTz60ffcdtoUkuKiAmjZvqzaVsqLn2/moYsP2ZuRy9C7REp4e21tLQkJCSiluOqqqxg5ciQ33HBDSG0y4e0W+XtqiYtykBgbOJfwthjez1rHMNMSQyc8+eST5OXlMX78eKqqqrjssstCbVLAidgpSUFpLVnJMUHf7kyMjaJfSiyr8ks585DhQb2XIbK54YYbQj6iCDYRO8IoKKslM8m/Cu09ZXh2Mmt3lOPymLR9hv2biBSMmgY3lXUuMpN7RzBG5CTh8vj4YVdlr9zP8FNCtdYWyQTjM4tIwSgo045UWb0kGMOykxCBVSY/RkiIiYmhrKzMiEYXUEpRVlYWcN+MiFzDKCi1BKOXpiSxUQ4GpMWzalsp5x8+qlfuafiRgQMHsnPnTgKVQ2V/ISYmhoEDBwa0z4gVDIdNSE2I7rxxgBjRL4nP1xfR6PIQExWRH1vE4nQ697pcG0JLZE5JSmvJSIrBHqQYkrYY0S8Zj0+xtqCi1+5pMIQbESkYO3pxh6SZXKv8gAl3N+zPRJxguDxeiioaem3Bs5kop53BGQlm4dOwXxNxglFYUY9PqV4fYYD2+txSVEVNQ9dqaRoMfYWIE4ydZXUAZCb1fmXrkf2S8Sn4brsZZRj2TyJOMHaVa8HICIFgDMpIwGm3mWmJYb8lIgUjIcZJbAi2Nh12G0OzElmVbxY+DfsnkScYZXWkJ/ae/0VrRuQks31PLRW1TSGzwWAIFZEnGOV1ZCT2/nSkmb3h7maUYdgP8ad6+yARWSQi663q7de10Wa2iFSJyCrruCMYxja6PJTXNpERgh2SZgakxhMbZTf5MQz7Jf4sBHiAm5RS34hIIrBSRBYopVqXZFqilDqhjesDxq7yeoCQjjBsNmF4v2RWbt1jyg8Y9jv8qd5eqJT6xnpcA6ynjULLvcHuEO6QtGRUTjJ7qhv3bvEaDPsLXVrDEJFcdFGj1tXbAQ4SkdUi8oGIjG/n+h5Vb2/eUk0P4QgDtGAAfLPVRE8a9i/8FgwRSQBeB65XSlW3evkbYIhSajLwMPBmW330tHr7rvI6kmKdxDh/WruhN0lLjCEjMYaVprq7YT/DL8EQESdaLF5QSr3R+nWlVLVSqtZ6/D7gFJGMgFqKFoxQjy6aGZmTzOr8MtxeX6hNMRh6DX92SQR4GlivlPp7O236We0QkRlWvwHfRgj1lmpLRvVPptHtZf1OE+5u2H/wZ5fkEOB84DsRWWWd+z0wGPYWZT4duEJEPEADcJYKcD61RpeHyjpX2IwwhmcnYRNYuWUPk4akh9ocg6FX8Kd6++dAh3uHSqlHgEcCZVRbFFXqWpZpvZhlqyNiohwMyUxk5dZSLjoy1NYYDL1DxHh6FlZoH4y0MBlhgF7H2FxYRVW9K9SmGAy9QuQIRqUWjPQwGWGA3l5VwLcmC5dhPyFiBKOoop5op5246PBJwDswPYG4KIfxxzDsN0SMYBRW1pOWEB1Wrtg2mzAiJ4mvt+wxNTMM+wWRIxgV9WGz4NmSUTkplNU0sX1PbahNMRiCTkQIhlKK4sp60hLCZ8GzmdEDUgD4anNJaA0xGHqBiBCM8tomXB5fSBPntEdyXBT90+L4apMRDEPfJyIEo8jaIQnHKQnAmP4prCuooLbRZBM39G0iQjD2+mCE4ZQEYMyAVHxKsXKL2S0x9G0iQjCKKuoR6NVaql1hcEYCcdEOs45h6PNEhGAUVtaTHBeF09675to8ddg8taA6jki12YRROcms2LwHn9leNfRhwscLqgMKK+p7bXQRV7aCtPx/k1j8KVENhQB47XHUp8+gcuApVAw+HWX/6dRozIBUVuWXsXF3FWOsnRODoa8REYJRVFFPblZiUO/hrN9F/9W/J2X3e/jssdSlTaUm+0iUOHA07SGuYg2DvrmefuvuYfeku6kcdBq0cCIb3T8ZAVZsLjGCYeizhL1guDxeymqbmDK86xm6/CV+zxfkLr8Im6eOstxzqRxwAsreKjO5UsRWfkd6/vMMWXE5ybveoWDaI/icWsjiY5wMzkzgy00lnH/4qKDZajCEkrBfw2gOaw9W0FnSrvcYvuQ0fI44Cqb8nYrBZ/xULABEaEidxM68+ygdNpfkwg8Z+enROOsK9jYZ0z+FTYVVpsiRoc8S/oIRxC3VxMKPGfLlJTQlDGdn3v244/xIhi52Kgeewu6Jd+FsLGTE/35OVM0WAMYMTAVgxRazW2Lom4S9YOwNaw+wl2dM1fcM+fISXPFD2D3xDnyO+C5d35AykV2T/oTNU8fwJafqNZDUOJJinXy1yfhjGPomYS8YRRX1OO02EmKcAevT7qogd+l5KHssheN/32WxaMaVMIzdE+/E7qpk2OenY3dXM3pACiu37sFjkgMb+iBhLxiFFfWkJQYwrF0pBn57M1ENuygcdwve6LQededKGEbR+N8RXbuVwSuuYEz/ZOqbPHxvkgMb+iARIBh1AV2/SCl4jZSdb1E+5GyakgKzm9GQMpHS4ReTXPQxs2uexm4TE4xm6JOEtWAopSiqbAhY0JmjcQ8DVv2OhqSxVAw6NSB9NlOVcxzVWbMZ8MNfOSF1rXETN/RJAlW9XUTkIRHZLCJrRGRKIIyrqnfR6PYGbEs157u7sHvqKBl1JUiAq6eJsGfkFbgShvKrpj/TWLplb5StwdBX8GeE0Vy9fSwwE7hKRMa1anMcMNI6LgUeC4RxP4a193xKEr9nKWk7XqFi4Km44wb1uL+2UPZoCsfeggMvN0U9yJcbi4JyH4MhVASqevvJwHNKsxxIEZGcnhr3Y2mBHo4wlI/+a27HHZ1JxeDTe2pWh3hisykbcTGT7Wuxf/tQUO9lMPQ2garePgAoaPF8Jz8VlS5Xb/8xD0bPBCOl4D/EVa6mLPdclD34QWw12UeyKWoGx9Q8Sv3u1UG/n8HQWwSqentbe54/ifPuavX2osp6EmOdRDm6v94g3iZy1v6FxoTh1GYd1u1+unZToWDYldQTi/e988FrMnEZ+gYBqd6OHlG0XBgYCOzuqXGByBSelv8CUQ27KMs9D6T3NoUGZKTwf96rSaz+Dr7+a6/d12AIJgGp3g68DVxg7ZbMBKqUUoU9Na6wop70Hix4ireB7A1/pyFpHA2peT01p0vYbVCXMZNlvoNRy+6Gyi29en+DIRj485PbXL39SBFZZR3Hi8jlInK51eZ9YCuwGXgSuLKnhrm9PkprGnuUOCct/0WcjUWU5569T+6K3mJiluLhpkvxYYOFV4DJxmWIcAJVvV0BVwXKKICSqgaU6kHQmc9D5sZ/0ZA0hoaUiYE0zW/GZijmSzrLUuYya/ujsOElGHtOSGwxGAJB2Hp69jSsPWXXO0TX76By4CkBtKprxDhgVJri6Yo5qLSxsOh6aCgPmT0GQ08JW8Eo7EktEqXI3PgwrtgB1KXPCLBlXWNilmJ3rY2iUddDYzksuTWk9hgMPSFsBaOooh6HTUiKi+rytQl7lhBXuUaPLnpxZ6QtJmbqdYvPqnJh1Onw3ZOwc0lIbTIYukvYCkZzpnBbNxYrs354CE9UKjXZhwfBsq6RGguDkhTLC7ww/kKI6wcLLgOvK9SmGQxdJqwFozvrFzGV35FYspjK/iegbF0fnQSDiZmK9SU+Kj0xMOVaKF8PKx8MtVkGQ5cJX8GorO/WDknGlqfw2WKozjk2CFZ1j4lZPhTwVYEH+h8E/Q+BZXdD9fZQm2YwdImwFIyaBjf1TZ4u+2DYXRWk7vgPNVmH4XMmBMm6rjMoCVJirGkJwAFX62pqi64PqV0GQ1cJS8FoDmvvqpdnav5L2HxNVPU/PhhmdRsRPS35epcXl0dBfD8YdwFsfhO2vBtq8wwGvwlLwehWlKrykbF1Hg1J43Al5AbHsB4wMUvR5IXVRdYoY9TpkJQLn14DbpNoxxAZ9BnBSCxeRHRdPlX95wTLrB4xKk0RbVcsa56W2J0w5Tqozocv7wmpbQaDv4SlYBRV1hMf4yAmyv9Kjulb5uFxplKbcVAQLes+Trt2FV+2w4NqjinJyoMhx8CK/wflP4TUPoPBH8JSMHSmcP9HF866HSQVfUx1zlFgC1z9kkAzMUtR3gCby1rULJl8Gdij4ZOrTHCaIewJU8Homg9Gev6/AaG63zHBMyoAjM9UCPw4LQGISYOJF8OOT2DDyyGzzWDwh7ATDK/PR0lVo/+ZwpWX1PwXqU87AE9M8Cq8B4LEKBiWqqcl+zDsREgbA4tvhKaq0BhnMPhB2AnGnupGfEr5PcJILP4fUY1FVGf/LMiWBYaJWT62ViiKa1tMS2x2mHI91BfDF3eEzDaDoTPCTjCKurhDkrr9RbyOJOrSpwfTrIAxOUuvUyxvOS0BSBsNw0+GVY9A8bchsMxg6JywE4y9Ye1+uIXbXRUk736PmqzDwnqxsyVZ8dAvQbF0u+enL078FUSnWNm5TDFnQ/gRfoJRUY9NhOS4zgUjpeANbD431f2O7AXLAsekLMWaYh/VTa12RaISYdJlUPQlfPdUaIwzGDog7ASjqKKe1IQo7LbOw9rT8l+gKX4oroRhvWBZ4Jic5cOnYMVO709fHHI0ZObBZ7dCfee1WwyG3iTsBGO3n1uqMVXriKtcQ3W/yFjsbMngZEiJVixtvVsCOvBkynXgqoHPbul94wyGDgg7wSisqPMrrD0t/0V84tTrFxGGTWBCVotgtNYk58LoM2DdM7Dz8163z2BoD3/qkswTkRIRWdvO67NFpKpFCYJu7wvWNLipbfR0GqUqPhepO16jPn06PmdSd28XUiZnKRo98G1hG9MSgHHn6+xcC68wldMMYYM/I4z5QGcRXUuUUnnWcXd3jSmsqAMgPbFjwUgsXIDDVR5xi50tGZWuiHUolu5oRzAcsXDAVVC2FkxRZ0OY4E/19s+AXsmNv7vcyoPRiWCk5b+IJyqN+tQDesOsoOCwwbgMxfICD15fOzEk/Q+BnINg6Z0mO5chLAjUGsZBIrJaRD4QkfHtNeqsevvu5hFGB05bjsZikooXUpN9BEj3izSHA5OyFZWNsKG0HZ8LEZ0DVPng40tNcJoh5ARCML4BhiilJgMPA2+217Cz6u2FFfUkxTqJcrYvBKnbX0WUl+rsyJ2ONDM+Q2EXWNbetAR0dq6Jv4btH8O6+b1mm8HQFj0WDKVUtVKq1nr8PuAUkYzu9LW7or7j6YhSpG1/kYakMbjjBnTL3nAi1gmj0nx8sb1Fjoy2GHEyZE6CRTdA7e7eM9BgaEWPBUNE+lkV3hGRGVafZd3pa3d5x3kw4iq+IaZmEzUREmjmD5OyFbtrFAVVHQiG2GDab8DbCAsuN1MTQ8jwZ1v1JWAZMFpEdorIxa0qt58OrBWR1cBDwFmqw5/LtmlyeymvbepwhJGa/yI+WzQ1mYd0tfuwZZIVjNamE1dLEgfChF/B1ndM3gxDyPCnevvZnbz+CPBITw1pzuPZnmCIp57UgjeozTgY5Yjr6e3ChpQYGJKst1fPmtRJ45GnQcFiWHQtDDkK4sI7/4eh7xE2np67O/HBSN79HnZPDTUR7HvRHpOyfPxQ6qOsvpMIVZsdpv9GJ9lZeIWZmhh6nbARjB9HGG2vYaTlv4g7JpuG5HZ3bSOWydn6i9/hbkkzyUNh/EWw6XVY/+8gW2Yw7EtYCUZslJ24NjKFO+t2kLDnc72VGuJq7MGgXzxkxnXg9dma0b/UuyafXG0cugy9Sth8+5q3VKWNau1p2/UiX032Eb1tVq8gohc/Vxd5qXP5Mc2w2WHG70B54f0LwOen0BgMPSR8BKO9LVXlI237SzSkTMITk9X7hvUSk7N9eHywYpefX/74fpB3Nez6DL7+W3CNMxgswkIwdKbwhjYXPOP3fEFUfUFEB5r5w9AUSIxqI6N4R+QeCwMOgy9ug5LVQbPNYGjG/9JiwWL+fGqq6jn2y++ZWpxO+po4xOdF2R2omBhSd72ItzYaZYsmZvf3eJMT8SUn4k1KBEdkx5K0xCa60NFXO724vQqnvfOMY4jAtBvho4vh/XPhvK/B0bUC1oY+SlMT7NkDZWVQWgoNDXDCCT3uNvSCcfHFpPh8XNdJsxzu+8k5b3ycFo/kBLwZabizM/A0H1nN/6aBMzISBE/KUizdCWuKvEwd4Od/TXSy3mpdcquua3LUo8E10hA+uFywYQOsWwfffw8//ADbtsGOHVBSsm/bxESoru7xLUMvGKtWsWDxGubt8PLbIYrkKAGbDfF6Saz6hIyy5ym3nYbXm4Y0urDV1eujth5bfYP1uAHntgJivlmLva5hn+6VCJ6sdNxDBuAaMgD34P64B/enadhgvFnp+lc6TBidrgs2L93RBcEAyDkQRp8Jqx+DgYfDmDODZ6QhdJSVwaJFsHQpLF8OK1dq0QCw26F/f8jOhunTITMT0tIgKQmSk+GgwNQcDr1gDB7MtvjdVCe7iU/34mvx/Y1v+gp3vywaYib7/cWWJhf2iqofj/IqHHvKcBSXEvPdD9gaGve29SYl0DRqKK6RQ2kaNZTGsSNw5w4AW2iWdqKsgs1Ld3i4amYUtq6I2cRLoHQtfPxryJ4CqSODZ6ihd1AKvvoK3n0XPvxQC4RSEB0NI0fCqafqf3NzYdAgiIpqv6/xgfFfCr1gAIWNikynnsc343TvJNb9A1VRx3ZpFKCio/D0y8TTrw23aaWw1dThKC7FuasY564inAWFxKxej82tFxu9CfE0ThxN46Qx+pgwChXbe+sCk7IUq4phU5mP0RldWKOxOWDm7bDgUnjnDDhnuVnPiESU0sLwyivw6qt6emGzwdixcOGFMG0ajBoVsml2WAjGrgYfGc59/Q+S6hegsFHvyAvcjUTwJSXgSkrANTL3x/NeL46SMqLydxG1dQdRW3cQv+wbAJTDTsOksdQfdAD1Mw/Q1wVxBDIhU2ET+GK7t2uCARCfDTNuhc9/D4tvgKMeC46RhsBTXg7//jc8+SSsXQsOB0ydCmefDQcfrKcWYUDIBUMpRVGjYljLz0O5Sar7lEb7GHySEHwj7HY8OVl4crKoP0in/ZO6BqK2FRC9cRvR6zeT8a/n4V/P40lNpn7mAdQdeRD1Mw9ARXcwDOwG8VE6R8bn2z1cNMXZpiNbh/Q/yFrPeNxazzgroPYZAsx338GDD8KLL+qdjdGj4YYb4Igj9EJlmBFywSivd9PkY58RRnzjV9hVDXWOaSGzS8XH0jRhFE0TRgHHYquqIXr9ZmLWbyb+s69I+mAxvtgY6g6dTu3PDqb+oCmoGD8rznfC5GzFK98rtlcqclO7sSg78RIoXQcfXwLp4yFzYkDsMgQIpWDhQvjrX+HjjyEmBo49Fk48EUaMCLV1HRJywfB4fcxMEQZH/ygYyXUL8EgKTfbhIbRsX3zJiTTMPICGmQeA10v0xm3EfrOOuKXfkPjxEnwx0dQedQjVJx1F4+SxPdp9mZytePV7+Hy7h9zUboxgbA44+E4d0frmSXDuCojrVhI0Q6D59FO4/Xa905GRAZdcooUiTKYcnSHdyHUTEKZNm6a+/vprqKqi6osv2VOtt0MdnmKGFF9OjXM2NVER4N3p9RK1aTtxX68hduVabI1NuAblUH3SUdQcfwTezLRudfv3L+34sPH4yT3I/VH2vU7rN+AQOO0jsEeGP0qfZMkSLRT/+5/e8jzvPDjuuN5bvJw2DRL09F5EViqlujV8DwvX8JYk1X8CQL1jaogt8RO7HdeYYVSedwpF9/2Wigt+gYqJJuNfz5N74sX0u/V+YlZ93+XcFXnZPrZVKHZV96CKe/o47QlasEg7dRl6nzVr9HTjsMP0YuY11+jFzZNOihiHwpaEfEqyD8pLUt1Cmuwj8dqSQ21Nl1HRUXo35aADsJeUEf/F18R9sZKET5fROHoYVWedSM3RsyCq8z+UvGzF6xv0tOTMiT1YWM09Fiq3wKpHIHMyTLqk+30Z/GfPHrjtNnjqKf3LfsUVWiRiInurO6xGGHFN3+LwlVMXKaOLDvBmpVN96rEU/+VmKs45CXt1Ddl//Ce5J11C6jOvYaut6/D6tFiduu/z/ACErk+6DLKnwydXws4lPe/P0D4uF/ztb3rxct487Vz1/PPwy19GvFhAmAlGUt0CvJJAo310qE0JGCo6ivpDp1Ny+zWUXnshnpws0h97gSEn/pq0x17AVtm+f39eto+NZT5KanswLQGdP+Og23VI/JsnQ9mGnvVnaJsFC7RH5c03w7hx8PTTcPXVEbOg6Q9hIxh2bwXxjSu0o1aEVzRrExGaxo6g7OoLKPndFbhG5ZI6/z/knvRrMh6ch730p9Uo86zUfV/4m4mrI6IS4dD7QYDX50BdUc/7NGiKi+Hcc+GYY6CxEe6/H+69FwYPDrVlAScQ1dtFRB4Skc0iskZEpnTHkMT6TxF8IfW96C3cg/tTfunZlNx+NY2Tx5D8yrsMOfVy0h+av8+IIyseBiQqPs/vQo6MjkjIgVn3QkMJvHE8uGoC0+/+is8HTzwBY8bAa69p1+2nn4YZM0JtWdAIRPX244CR1nEp0HV/ZOUlue4jmmxD8drSu3x5pOLJyaJi7ukU33UdjXljSXnhLXJPuYy0J15CanVS5MnZPtaV+CjvLKO4v6SNhoPugD1r4M1TwNPY6SWGNli7FmbNgssu08FfTz0Fc+d2HADWBwhE9faTgeeUZjmQIiI5XTHCUbsCp7eEWmffVeaO8GamUTH3dEpuv5qm0UNJe+oVck+5lJTn3mBKciMK/E8Q7A85M2H6b6HgU3jnl+B1B67vvk59Pfzud3DAAToHxS23wN//3ienH20RiDWMAUBBi+c7rXM/ob3q7VEVb+ORJBrtYwNgTuTiycnSU5VbL8c9KIeMR57joPMv4+xv32XZlgCPBHKPgSnX60pqH5hEwn7x0Ud6UfO+++Coo+DZZ2HOnLDKqdIWz+a7WPB9YNasAuGH0dan1aaXklLqCeAJ0J6eAFRuwlm3kmrnz/rmYmc3cA8ZQNnVFxC1eTtJby1k7n8fZ/f/3qK+8WLiTj0icNGyI04GTz2seQIc0XDM03pHxbAvJSU6IOzFF3Xeib//XY8wIgClFG/vdjM7tYajA9BfIARjJzCoxfOBgP8lxtc/hcJOnbPvL3Z2FdeIIZTe+CsqV+cT++anxN3wZ3jqZfjdpXDotMD8so05G7wuWDcffB6YM1/Hohi0d+78+XDTTVBTAxdcoHdDImidotylqPXC4PTAlBcNxE/V28AF1m7JTKBKKVXo15WuGtj4Ip7oSb0Txh6JiJA8eSi3X/UA/zn7Kigph/N+C2ffBKsD5E8x/kKYcDGsfwHeP8+saQBs2gQ/+xn86lcwcKDOU3HRRRElFgDb6/VAfkhaYASj058Sq3r7bCBDRHYCdwJOAKXU48D7wPHAZqAeuMjvu3//PLhrcKccDOZvtF1EYHpsNfPGzuHoY4eRvGgJ/HchnHgFHH84/PZiGDao8446Ytx5YHPCmsf1zsnPXwJnbGDeQCThcsEDD8Cf/qRjPW64QWfbDlHaxp6y3dpdG5IeH5D+AlG9XQFXdevu3z4CSSPwOQaD29WtLvYXDoyp4IOGbJY0pXLC8YfDEQfCu4v18dESOPN4uP5C6NeDMPYxZ4I9Cr59GF4/Fk55G2JSAvQOIoBly+DSS/WW6ezZ2kszPbK3+XfU+4i3Q0pcYALdQiubRz8OY34V9qvM4cAgewP97Y0sqrambrExcMYc+Ocf4OhD4NUP4LDz4N4noLIHDlkjT4WZt0HhMnjlMKj1fzkqYikt1f4Uhxyig8b+8he4886IFwuA/Hof/aNU1zO3tUNoBaP/wZBmskH5gwjMiC5nbUMcJe4WA8OURLjoF/D3W2HaBHj8ZTj0HHjsJWhs6t7NBh8Jh96no1xfOBCKvwnMmwg3vF547DGdVPfpp+G003TA2MEH964dQcpJo5RiR52PnMAkggNCLRiGLnFgTAUA/6tpY4E4OwOuOQ/uu0mvZ9z7hB5xvPQeeLrhY5E9FY74J/jc8PIs2Ph6D60PM5Yu1fU7rrxSe2o++SRcdRXEBWZxsEuUvAZb7wJfYKfl5W69Q5ITFThBMoIRQWTbXQx11LG4uoPksLkD4NZL4c6rIDEebvkrHP0rePtT/YvaFVJH6EpqycPgndNh2d1B+zXsNbZuhXPO0dOPnTvhjjt0OPrQoaGxx+eColfAXQ22wO7A7KjT/1dmhLEfc2B0BZubYihwdbKINW4E/Ok6uOkinY366j/BkXPhtQ/B3YVgtpg0mP13GHI0LL0T3jsb3B3n8ghL9uyB667TgWJvvKH9KZ59VmfnDuUaWtmH4CmHrMBXq2veIelvBGP/ZXp0BYLqeJTRjAjMmAQP/FbvoPh8cNP9cPj58Pzb0OjnENgeBTN+B5MuhR9e1esakZJTo65OL2IOHw6PPKJD0J9/XiffjQ3xtrHyQtHLEDUYEiYHvPv8eh9xNkVSAJ13jWBEGKl2D6OdtSyqTvR/dmCzwUF5cP/N8NtLIDYa/vAgHHQm/O0Z7QzWGSLaK/Sw+6F2F7wwDda/1JO3Elyqq3XMx9ChOlXepEl6QfPmm3US3nCgfAG4CiHpqKCMcrbW+hgQrQLatRGMCGRmdDm73FFsaOxiyjcRmDpeT1VuuwKG9Id/Pgczz4Tr7vHPc7TfdDj6Cb2u8f45sPDK8AqRLyrSW6JDhuio0qFD4eGH4c9/1ufCBZ8bdj8LUYMgZkLAu/cqxbZ6HwMDOB2BcEsCbPCL6dGVvFQ3iI+rkhgb240vqwhMHKWPwj3a8evDJfDfBTB5DJxxLJx0JKS0k1ouLlOva3z3lK4YX7gcjn8R0sf07I31hJUr4Z//hJdfBrdb56o47zxdSSwcKfsAXEWQcWlQRhe7GhQuHwyIVrQdH9o9zAgjAom1+ZgaVcGi6gQafT38Y8jJhLm/gEfvhLmnQkU13PZPmHIaXHYHfPxF24ukNgdMvhxm/QWqtsK/p+jyjL25i1JaqkcP06bp4/XXtRv3c89p1+5wFQufCwqfh6ihEBMckd1apxc8B0YH9v/DjDAilFkx5SxtSueLmgR+lhyAVHtxMXDcYfrI3wWfrYDPv4EPlmjnsCNnwjGHwOEzIL7FYmH/g3VY/Ir7daW1re/DsU/rUUgwKCuDd9+F//4X3n9fjyZGjtT1Po49FuIDEzMRVPa8De5SyPxl0HZottb6sKPICXCsnBGMCGWUs5ZMWxMfVCUFRjBakjtAH+ecCGs2wLJVsGApvLEAnA6YOgEOzoODDoC8MRCbrj1DN72hc2s8OwmOm69rovQUlwtWrIDFi3VW7s8/1/4kWVlwyilaJIaHT0nNTvE2QNGLED0SYkYG7TZb6nz0iwJHgPXICEaEYhM4PLaU/9QNYHtTFEOigxC857DDlPH68Hrhh22wch2s2wwPPgt/nw8OB4wZqtdDJoyEzGuh7BV4bQ5Mux4OvRccfizOKqWnGNu26Yrmq1frY8UKaNBlNBk+HM4+W69PjBoVmTFIxS+BpwLSzg/qbbbWehkZE/jpoRGMCGZWTDlv1uXwXmUyV2bv6fyCnmC3a2ewcVZ18do6WL8VNuXDtp06aval91q0F0j8ByQ9DkMPgKyBWlzsdn00NOitz5oandGqoECn6G8mNhaGDdP1R/Py9LZocuRVw9uHpkLtdxE3BaKD51la6VKUu2FAshEMQwuSbB6mRVeyoDqZizJLibX14oJjQjxMn6gP0COEskooKoXiMijeA6UFULEV8pfB5lSQeO085vPpRDTx8boaWP/+OuVdVhZkZ+ut0JyciM1B0S47HwNskHxiUG+zpU6HAAR6SxWMYEQ8R8aWsrwpjYVVSZyYWhU6Q0QgI1UfE1rMzb21UPEKNKyFhCEw9FaIyg6dnaGi+muoXALJPwdHSlBv9UON3iEZHOAdEjDbqhHPcEcdwxx1vF6Rgjcc48LsCZD+K0g9C+rWw7pfQdmCyA9i6wrKAwUPgyMDEg8P+u021PjoF6WIDUI+ZyMYEY4IzIkrptAdxRe1YZoXVQQSDoR+N4MzG/LvgW1/BE/7dWX7FEUvQeMOSDkZJDCZr9pDKcUPNV6GBGF0AUYw+gRToqrItjfySllqeP9wOzIg82o9LK/4HNZdBFVfhdqq4FK/UbuAx02B2MC7gLemuElR6YbcIOyQgBGMPoFNYE5sCZubYviyLswdl8Smg62yrweJgs23wI6HwBdG8SiBwueCbffqaVnKL3rlls3rF0YwDB1ySEwZ2fYm5u1JD8+1jNZEDYTsGyHhMNjzX/j+Uqj7IdRWBZbd86AxH1LPBHvvCPmGGi8OUQHNgdESvwRDROaIyA9WhfZb23h9tohUicgq67gj8KYaOsIhcGrcbra7ovnUn1wZ4YA4IfVUyLwCPDWw4SodY6H6QNnGmlVQ/CrEHwSxvVcC9IcaH4OjA+/h2UyngiEiduBf6Crt44CzRWRcG02XKKXyrOPuANtp8INp0ZXkOup5pjSdOm8EDR5jRkG/30DcZP2r/MO10Lgr1FZ1H9ce2PpHcGRCykm9dlu3T7Gp1seQIE1HwL8Rxgxgs1Jqq1LKBbyMrthuCDNsAucmFFDucTCvNMJS5NviIP18fTTkw/qLYc87kbf96nPphL7eBsi4CGxdzFnSAzbW+nD5YHiIBcPf6uwHichqEflARMa31VF71dsNgWO4s56jYvfwbmUKa+ojsHJZ3BTI/g04h8COv8OW34Pbj4xg4YDyQf59UPc9pJ0Nzn69evs1lXoqNyI2tILhT3X2b4AhSqnJwMPAm211pJR6Qik1TSk1LTNc0qT1QX4RX0i2vYm/7O7HHncEOvM6UiDzMkg5FapXamevys9DbVXHKAU7H4eKRZB8gp5e9TJrqrz0j1IkBvG/3B/B6LQ6u1KqWilVaz1+H3CKSA9q9hl6QrT4uDppK40+4a5dOdT3NMlOKBAbJB6md1JsibDldv3r7Qmh+3t7KAWF83V9kYRDIfHIXjfB41Osq/YxMoijC/BPMFYAI0VkqIhEAWehK7bvRUT6iVWLTURmWP2WBdpYg/8McDRyaWI+W5ui+c2OgVR4guAn3Bs4+0H2dZB0NJQthLUXQOmH4bO2oRTs+j8ofA7iD4SUU0ISdr+x1keTj9ALhlLKA1wNfASsB15VSq0TkctF5HKr2enAWhFZDTwEnGUVaTaEkLzoaq5N2kqBK4qrtg/is5qEsPmedQlxQPLxerRhT4ft98PG66Fhe2jt8rn0qKf4FUg4BFJ/qUdGIWBNVfDXL8DPaFVrmvF+q3OPt3j8CPBIYE0zBIJJ0dXcYtvIM7VD+MvuHAZHNTErsZbcKBfJDi9eJdT5bFR47FR67dR47XiVEGXzkeXwMDqmkbGxjdjDYVYT1R+yroa6r6DqHVh/CWT9AvqdC452EhYHC1cJbL0b6tZB0hxIOiakCX2+rQj++gWY8Pb9glxnA3ekbOCLxjSWNaXxUlkaqo21bEERJ9pTsFHZaFJ6GpNo83J8ShWnplaS6gixU5XYIGEmxI6Hqveg+DUofR/6naPFwxYkF8dmlIKKxbDjQT3CSL8A4g4I7j07oc6j+K7ax5EpwR8+GsHYT7ALHBZbzmGx5TT4bJT5oqjxOXCIIla8JNk8JIgHm6UjSkGNcrDRncCXjSm8Wp7K2xXJXJxZxs9Tqva2Cxn2REg7S7uWV70Lu57QU4Os0yDz5OCMOBp3ws5HoWqZrieSeT44Q7/bt7LCi1fBhHhf0O9lBGM/JNbmY6Ct42AvEUgSndFrWnQlhZ5oXqwdyCMlWSytjed3/YtIsgf/D7RTovpD5qXQtAWqP9GeokUvQtrRkH4MxI/v+VShsUCLUemH1nrKiTqvhYTHQvKX5V7ibYqhveAjZgTD4Bc5jiZuTN7C/xrTebF2INdsH8SfB+5mUJQ71KZpoodD5nBw7YaaRbrIcek7ED0AUg6FpKmQMMF/z0tXqR5JlH8Ktau0UCTMhKRj9egmTPAqxVflHsbFq15ZZzKCYfAbEZgdW8ZARwOPVA/j5h0DuW/QLoYGI2N5d4nqD+nngu80qF8N9Sv1Okfxy/pLHzMUYgZC9EBwpuhzYgdvnV7IdBVD/VYtPKBzeCQdBwkHhZVQNLOhxke1BybE9872lxEMQ5cZ4aznluRNPFA1gt/sGMg9g3YxKqYp1Gbtiy1GZ/lKOBB8TdC0FZo2grsQatbohcvWDssSDY40LRLJ03RVMme/sC5n8NkeDw5RjIszgmEIY3IcTdyasom/Vo7gloIB/HnAbsbHhWkSHFu0DjFvGWauPFbSHp+OARGnDoALY3FojcenWFTiYUK8Iq6XllMiKAbaEG5k2V3cmrKJRHHz+50D+K6+9yIze4w4dCYse5KOXbHHR5RYAKys9FLlgRmJveeNZwTD0CPS7G5uSd5Eis3FbZEmGhHOJ8UeEuyK8b20fgFGMAwBIMXu4bfJm0g1otFr1LgVy8q8TElQQcuu1RZGMAwBIcXu4bcpWjT+sHMAK+viQm1Sn+bdQjcuBbOSe9cXxgiGIWAk27RoZNmbuH1nfxZWhd82ZF/A5VO8udvNuDgfA4LsCd8aIxiGgJJs83BL8kZGOmt5oKgf/1eSERlZzCOIT0o8VLrhZ6m9/8EawTAEnDibjxuTt/CzmD28UZHKDTsGsqMpuBW/9hdcPsUrBW4GRStGBzmUvS2MYBiCgkMU5ybu5PLEbexqcnJF/mD+rySDygAm8nH5hN0uJ7tcTkrcDlyRmFmsi/x3l5vCRsXJ6b6Q7AIbxy1DUJkRU8noqFper+vPfyvSeLcymVkJtRyeVMvE2AbiOwhgcysocTspdjsodjspdjspdDv2Pi/3/nTUkm53MyzGxbjYBqbG1TMqpinS3CvapazJx4s73EyK9zG2F7dSW2IEwxB0km0efpW4g+Nii1nYkMny2jQ+rUnChiLT4aZflIc4mw8F1Hlt1HrtVHttlHsd++TtsKFIs7nIsLsY52wgI9pFmt2FHYVL2ajyOSn2RlPQGMuKugyeBfo7XRyVVMPPkqvp5/SE7DPoKT6leHBTEx6lOC0zdFHCRjAMvUaOo4nzE3dyVsIutrjj2ehOoNAbTZkrijJlRwTixEuKuOjv8JIe7SLT7iLD5iLd3kSqze13RGa1z8EaVxJLG9N4riyd58rSmRJXxymplUyPrw99Po8u8nKBmxUVPs7K9JERwuUgIxiGXscpijFRtYyJqg3aPZJsHmbFlDMrppxSr5Oljeksbszgjl0D6O90cUpqJUcnVxNnC/8tnMUlHp7f7mZ6oo9ZyaG11wiGoc+TYXdzUnwRx8cV8XVTKgsbMnm0JIv5penMSa7mpJRKcqLCc7ryUZGbf2xyMTxWcXZWaBY6W2IEw7Df4BCYGVPBzJgKtrjjWNiQyVsVqfy3IoWZCXWcklLJxLiGsEh43OBVPL7FxYfFHsbE+risv4+oMNjT9EswRGQO8E/ADjyllLqv1etivX48UA/MVUp9E2BbDYaAMdxZz3Dndn7p3c2ixgwW12WwrHYgKXYPByfUcUhiLZNiG4jq5SlLrUfxcbGHVwpcVLnh2FQfP0/3hYWIgR+C0aJ6+9HoKmgrRORtpdT3LZodB4y0jgOBx6x/DYawJtXu5hfxhZwQV8S3Tcl840rhk+ok3q9KxoFiWHQTY2IbGRnTSI7TTbbTQ7rDE5AvsE9BhddOocvJpiYfq2sa+brci1vB6Fgfl2T7GBpm5XH9GWHsrd4OICLN1dtbCsbJwHNW8aLlIpIiIjlKqcKAW2wwBIEoURwYU8mBMZW4lLDelcgmdzxbPfF8VJXI25Upe9s6UCTYvSTavCTafcTafDhER406ReEUhaBQCD60MPgQPEqo99mo9wm1Xjt7PA5cqnme4SPdqTgkWTEj0ceQMA349Ucw2qre3nr00F6F930EQ0QuBS61njaJ3bm2S9aGngygNNRGdIFIsxf2c5tfCkQnnTO6uxf6Ixj+VG/3pw1KqSeAJwBE5Gul1DQ/7h82RJrNkWYvGJt7AxH5urvXBqR6u59tDAZDhBOQ6u3W8wtEMxOoMusXBkPfo9MpiVLKIyLN1dvtwLzm6u3W64+jCzUfD2xGb6te5Me9n+i21aEj0myONHvB2NwbdNte0RsbBoPB0Dlh4DtmMBgiBSMYBoPBb4IuGCIyR0R+EJHNInJrG6+LiDxkvb5GRKYE26aO8MPecy0714jIUhGZHAo7W9nUoc0t2k0XEa+InN6b9rVjS6c2i8hsEVklIutE5H+9bWMrWzr7u0gWkXdEZLVlrz/reEFFROaJSImItOnv1K3vnlIqaAd6kXQLMAyIAlYD41q1OR74AO3LMRP4Mpg2BcDeg4FU6/FxobTXX5tbtPsUvUB9erjbDKSgvYkHW8+zwtze3wP3W48zgXIgKsSf82HAFGBtO693+bsX7BHGXrdypZQLaHYrb8let3Kl1HIgRURygmxXe3Rqr1JqqVKqwnq6HO1zEkr8+YwBrgFeB0p607h28Mfmc4A3lFI7AJRSobTbH3sVkGgFYiagBSOkMfNKqc8sO9qjy9+9YAtGey7jXW3TW3TVlovRCh1KOrVZRAYApwKP96JdHeHP5zwKSBWRxSKyUkQu6DXrfoo/9j4CjEU7LH4HXKeUCl0uPf/o8ncv2PkwAuZW3kv4bYuIHIEWjFlBtahz/LH5H8AtSimvhDoDi8Yfmx3AVOBnQCywTESWK6U2Btu4NvDH3mOBVcCRwHBggYgsUUpVB9m2ntDl716wBSPS3Mr9skVEJgFPAccppcp6ybb28MfmacDLllhkAMeLiEcp9WavWPhT/P27KFVK1QF1IvIZMBkIhWD4Y+9FwH1KLw5sFpFtwBjgq94xsVt0/bsX5EUXB7AVGMqPi0XjW7X5OfsuvHwVwkUif+wdjPZoPThUdnbV5lbt5xP6RU9/PuexwCdW2zhgLTAhjO19DLjLepwN7AIywuDvI5f2Fz27/N0L6ghDBc+tPJT23gGkA49av9geFcJIRT9tDiv8sVkptV5EPgTWAD50preQpEPw8zP+EzBfRL5DfwFvUUqFNExfRF4CZgMZIrITuBNwQve/e8Y13GAw+I3x9DQYDH5jBMNgMPiNEQyDweA3RjAMBoPfGMEwGAx+YwQjCFgRoatEZK0VwZhine8vIv/p4Lrc9iIL+xoicpeI3Gw9vltEjuqgbZ6IHN+NeywWkZ9seYtIvohkdLU/gxGMYNGglMpTSk1AB/9cBaCU2q2UCnloeUtEJGC+OFa4dJf/ppRSdyilFnbQJA/tL2AIMUYwgs8yrICeliMIERkvIl9ZI5E1IjKy5UUiMkxEvhWR6a3O54jIZy1GMIda5+eIyDdWPoZPrHNpIvKm1f9yy6W9+df9CRH5GHhORDJF5HURWWEdh7R+EyIyV0TeEpEPrbwQd7Z4T+tF5FHgG2CQiPzG6meNiPyxRR9/sK5dSIvaGCIyX6wcHaJzdiy13sdXIpIM3A2cab3nM0UkXnSuhxXWZ3SydW2siLxs3fcVdAxKe/zG6v8rERkhIokisk1EnFZfSdZIxNnp//D+RKhdV/viAdRa/9qB14A5rd10gYeBc63HUeg/7ly0C/Ro4Fsgr42+bwL+0KL/RHT+hQJgqHU+rcU97rQeHwmssh7fBawEYq3nLwKzrMeDgfVt3HcuujBVumXrWnSMSi7aE3Om1e4YdJJZQf8gvYvOyzAVHcUZByShvQtvtq6ZD5xufQ5bgenW+SS0W/Zc4JEWttwDnGc9TkHHl8QDN6K9MAEmocPLp7XxXvJbfIYXAO9aj58BTrEeXwr8LdR/S+F2mOrtwSFWRFahv0wrgQVttFkG/EFEBqLzPmyyXM0zgbeA05RS69q4bgUwz/rle1MptUpEZgOfKaW2ASilmnMgzAJOs859KiLp1i82wNtKqQbr8VHAOPkxkjVJRBKVUjWt7r1AWcF2IvKG1f+bwHal8ymAFoxj0IIHOjfESLSw/VcpVW9d37pUBWihLFRKrbBsrrbatm53DHBS8xoIEIMWusOAh6xr14jImjbu0cxLLf590Hr8FPBb6z1dBPy6g+v3S8yUJDg0KKXygCHoX82rWjdQSr0InAQ0AB+JyJHWS1Xo0cJPpgXWdZ+hvxi7gOdF54kQ2g5L7ih8ua7FORtwkNLrLnlKqQFtiEXLazvqS4B7W/Q1Qin1dDvXt2WvP7EKghbU5nsMVkqt9/MerW3f+1gp9QWQKyKHA3YVotiVcMYIRhBRSlUB1wI3t54Li8gwYKtS6iF0IahJ1ksu4BR0YahzWvcpIkOAEqXUk8DT6BRsy4DDRWSo1SbNav4ZcK51bjY6XLyt/AwfA1e3uEdeO2/paGtdJNay8Ys22nwE/EpEEqy+BohIlmXLqdY6QyJwYhvXbgD6N6/bWOsKDqAGPUJpeY9rxBp6iMgBbbzfCfz4mbbFmS3+Xdbi/HPoUcczHVy732KmJEFGKfWtiKxGV4xb0uKlM4HzRMQNFKEX9pKsa+pE5AR0EpY6pdRbLa6bjV6wcwO1wAVKqT2iC12/Ye1SlABHo9cqnrGG5vXAhe2YeS3wL6udA/3Fu7yNdp8DzwMjgBeVUl+LSG6r9/uxiIxFJ7zBsvE8pdQ31kLkKmB7q8+i+VqXiJwJPGyJUgN6urQIuNWa5t2Ljgz9B7DGEo184AR0iHnz+11Fx7kookXkS/SP5tktzr8A/Jleq4scWZhoVYNfiMhc9ALi1Z21jWSs3ZqTlVLnh9qWcMSMMAwGCxF5GJ0J3vh8tIMZYRgMBr8xi559FBG5XkTignyPDl3dg3zvva7lPWlj6BpGMCIY6dit+3q0k1TQUGHo6m4ILkYwwhQRqRWRv4l29/5ERDKt84tF5B7RpQOvE5GfWe7R31nu0tEici3QH1gkIous644RkWVWf6+12Pb8yfXW+XzrPstE5GsRmSIiH4nIFrFyWcq+ru52Efmr1c8aEbmmjfe0WEQeFO3avl60G/gbIrJJRP7cot2Not3e14rI9S3Ot+daPly0y/pKEVkiImMC/h9i0ITa1dQcbR9oZ6Jm1/E7sFyjgcXAo9bjGLST1yjr+XPA9dbjfKys1ejSAp8B8dbzW6w+O7v+Cuvxg+hkvM1u6CXW+Vx+dHW/Al1ZzWE9T2vjPS3mx3KC16FT2ucA0eiU9+n86EIej/YSXQccQMeu5Z8AI63HBwKfWo/vam5jjsAcZpckfPEBr1iP/w280eK15vOjgW3qx+I+z6K9Sv/Rqq+ZwDjgC8s3IgrtrNTZ9c3u298BCUp7f9aISKNYIfstOAp4XCnlgX3c01vTss91SqlCABHZiq6RMQvtQl5nnX8DOBQ9Gv6vauVabo2UDgZea+FCHt3OvQ09xAhG5NByO6vZFdvfMmaCjgM5e5+T7Xt0NtNk/etr8bj5eeu/HX/dujvrs6P31Fb/NqBSaVd8Q5Axaxjhiw0dwQm6MPHnbbTZgI59GGE9Px/4n/W4pTv1cuCQ5nYiEiciozq5vqt8DFzevBDbwj29q3wGnGLZGI+uCbuEdlzLlXZ13yYiZ1j3FRGZ3M17GzrBjDDClzpgvIisRAekndm6gVKqUUQuQg/HHehI1ubCRU8AH4hIoVLqCMtT86XmRU3gNqXUxg6u7ypPoQsor7Hc1p9EFyjuEkq7kM/nR7fup5RS3wJ04Fp+LvCYiNyGLtTzMro6mSHAGMetMEVEapVSCaG2w2BoiZmSGAwGvzEjDIPB4DdmhGEwGPzGCIbBYPAbIxgGg8FvjGAYDAa/MYJhMBj8xgiGwWDwm/8P2JZy/sVbQuAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "for endpoint in ['F2', 'I2', 'S1']:\n",
     "    fig, ax = plt.subplots(figsize=(4,4))\n",
@@ -4081,9 +16765,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 330,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "def plot_score_hist(ax, data, label, color, bins=10, kde=False, rug=True):\n",
     "    ax = sns.distplot(data, ax=ax, label=label, color=color, bins=bins, kde=kde, rug=rug)\n",
@@ -4110,9 +16831,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 331,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x864 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(12,12))\n",
     "for i, endpoint in enumerate(['F2', 'I2', 'S1']):\n",
@@ -4282,6 +17016,20 @@
    "outputs": [],
    "source": []
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,