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Creating 10.21105.test_journal.00029.crossref.xml
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121 changes: 121 additions & 0 deletions test_journal.00029/10.21105.test_journal.00029.crossref.xml
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<full_title>Journal of Open Source Software</full_title>
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<title>Nimbus: a Ruby gem to implement Random Forest
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Machine Learning, 45(1), 5–32.
https://doi.org/10.1023/a:1010933404324</unstructured_citation>
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<article_title>A framework for sensitivity analysis of
decision trees</article_title>
<author>Kamiński</author>
<journal_title>Central European Journal of Operations
Research</journal_title>
<doi>10.1007/s10100-017-0479-6</doi>
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<unstructured_citation>Kamiński, B., Jakubczyk, M., &amp;
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237 changes: 237 additions & 0 deletions test_journal.00029/10.21105.test_journal.00029.jats
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<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2 20190208//EN"
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<journal-title>Journal of Open Source Software</journal-title>
<abbrev-journal-title>JOSS</abbrev-journal-title>
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<article-id pub-id-type="doi">10.21105/test_journal.00029</article-id>
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<article-title>Nimbus: a Ruby gem to implement Random Forest algorithms
in a genomic selection context</article-title>
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<contrib contrib-type="author">
<contrib-id contrib-id-type="orcid">0000-0001-7699-3983</contrib-id>
<name>
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<institution>Departamento de Mejora Genética Animal. Instituto Nacional
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<kwd>random forest</kwd>
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<sec id="summary">
<title>Summary</title>
<p>Nimbus is a Ruby gem implementing Random Forest in a genomic
selection context, meaning every input file is expected to contain
genotype and/or phenotype data from a sample of individuals. Other
than the ids of the individuals, Nimbus handle the data as genotype
values for single-nucleotide polymorphisms (SNPs), so the variables in
the classifier must have values of 0, 1 or 2, corresponding with SNPs
classes of AA, AB and BB.</p>
<p>Nimbus provides a novel dataframe of random forest under ruby, and
implements a modified algorithm that can separate all genotypes for a
single marker, which can accommodate both additivity and dominance.
Further, it allows the user to specify a loss function and provide
full information of the trees in a .yml file.</p>
<p>Nimbus can be used to:</p>
<list list-type="bullet">
<list-item>
<p>Create a random forest using a training sample of individuals
with phenotype data.</p>
</list-item>
<list-item>
<p>Use an existent random forest to get predictions for a testing
sample.</p>
</list-item>
</list>
<sec id="random-forest">
<title>Random Forest</title>
<p>The random forest algorithm is a classifier consisting in many
random decision trees, it was first proposed as a massively
non-parametric machine-learning algorithm by Leo Breiman
(<xref alt="Breiman, 2001" rid="ref-Breiman2001" ref-type="bibr">Breiman,
2001</xref>). It is based on choosing random subsets of variables
for each tree and using the most frequent, or the averaged tree
output as the overall classification. Random forest makes use of
bagging and randomization, constructing many decision trees
(<xref alt="Kamiński et al., 2017" rid="ref-Kamiski2017" ref-type="bibr">Kamiński
et al., 2017</xref>) on bootstrapped samples of a given data set.
The prediction from the trees are averaged to make final
predictions.</p>
<p>In machine learning terms, it is an ensemble classifier, so it
uses multiple models to obtain better predictive performance than
could be obtained from any of the constituent models.</p>
<p>The forest outputs the class that is the mean or the mode (in
regression problems) or the majority class (in classification
problems) of the node’s output by individual trees.</p>
<p>The algorithm is robust to over-fitting and able to capture
complex interaction structures in the data, which may alleviate the
problems of analyzing genome-wide data.</p>
</sec>
<sec id="learning-algorithm">
<title>Learning algorithm</title>
<p><bold>Training</bold>: Each tree in the forest is constructed
using the following algorithm:</p>
<list list-type="bullet">
<list-item>
<p>Let the number of training cases be N, and the number of
variables (SNPs) in the classifier be M.</p>
</list-item>
<list-item>
<p>The mtry number of input variables is told to the algorithm
to be used in determining the decision at a node of the tree; m
should be much less than M (usually 1/3), and the optimal value
should be tuned for methods like grid search.</p>
</list-item>
<list-item>
<p>Choose a training set for this tree by drawing n samples with
replacement from all N available training cases (i.e. bootstrap
sampling). The rest of the cases (Out Of Bag sample) will be
used to estimate the error of the tree at predicting the classes
of this out of Bag samples.</p>
</list-item>
<list-item>
<p>For each node of the tree, randomly choose m SNPs on which to
base the decision at that node. Calculate the best split based
on these m SNPs in the training set.</p>
</list-item>
<list-item>
<p>Each tree is fully grown and not pruned (as may be done in
constructing a normal tree classifier).</p>
</list-item>
<list-item>
<p>When in a node there is not any SNP split that minimizes the
general loss function of the node, or the number of individuals
in the node is less than the minimum node size then label the
node with the average phenotype value of the individuals in the
node.</p>
</list-item>
</list>
<p><bold>Testing</bold>: An independent sample can be pushed down
the tree to predict the most probable phenotype given the SNP
genotypes. It is assigned the label of the training sample in the
terminal node it ends up in. This procedure is iterated over all
trees in the ensemble, and the average vote of all trees is reported
as the random forest prediction.</p>
</sec>
<sec id="nimbus">
<title>Nimbus</title>
<p>Nimbus trains the algorithm based on an input file (learning
sample) containing the phenotypes of the individuals and their
respective list of genotype markers (i.e. SNPs). A random forest is
created and stored in a .yml file for future use.</p>
<p>Nimbus can also be run to make prediction in a validation set or
in a set of data containing yet to be observed response variable. In
this case, the predictions can be obtained using the random forest
created with a learning sample or using a previously stored random
forest.</p>
<p>If a learning sample is provided, the gem will create a file with
the variable importance of each feature (marker) in the data. The
higher the importance is, the more relevant the marker is to
correctly predict the response variable in new data.</p>
<p>Nimbus can be used for both classification or regression
problems, and the user may provide different parameter values in a
configuration file to tune the performance of the algorithm.</p>
<p>-<inline-graphic mimetype="image" mime-subtype="png" xlink:href="nimbus_outputs.png" /></p>
</sec>
</sec>
</body>
<back>
<ref-list>
<ref-list>
<ref id="ref-Breiman2001">
<element-citation publication-type="article-journal">
<person-group person-group-type="author">
<name><surname>Breiman</surname><given-names>Leo</given-names></name>
</person-group>
<article-title>Random forest</article-title>
<source>Machine Learning</source>
<publisher-name>Springer Nature</publisher-name>
<year iso-8601-date="2001">2001</year>
<volume>45</volume>
<issue>1</issue>
<uri>https://doi.org/10.1023/a:1010933404324</uri>
<pub-id pub-id-type="doi">10.1023/a:1010933404324</pub-id>
</element-citation>
</ref>
<ref id="ref-Kamiski2017">
<element-citation publication-type="article-journal">
<person-group person-group-type="author">
<name><surname>Kamiński</surname><given-names>Bogumił</given-names></name>
<name><surname>Jakubczyk</surname><given-names>Michał</given-names></name>
<name><surname>Szufel</surname><given-names>Przemysław</given-names></name>
</person-group>
<article-title>A framework for sensitivity analysis of decision trees</article-title>
<source>Central European Journal of Operations Research</source>
<publisher-name>Springer Nature</publisher-name>
<year iso-8601-date="2017-05">2017</year><month>05</month>
<uri>https://doi.org/10.1007/s10100-017-0479-6</uri>
<pub-id pub-id-type="doi">10.1007/s10100-017-0479-6</pub-id>
</element-citation>
</ref>
</ref-list>
</ref-list>
</back>
</article>
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