Fix doc issue

docs/references.bib

@@ -16,33 +16,6 @@ @ARTICLE{Moinfar2024-cx
   author   = "Moinfar, Amir Ali and Theis, Fabian J",
   journal  = "bioRxiv",
   pages    = "2024.11.06.622266",
-  abstract = "Single-cell genomics allows for the unbiased exploration of
-              cellular heterogeneity. Representation learning methods summarize
-              high-dimensional single-cell data into a manageable latent space
-              in a typically nonlinear fashion, allowing cross-sample
-              integration or generative modeling. However, these methods often
-              produce entangled representations, limiting interpretability and
-              downstream analyses. Existing disentanglement methods instead
-              either require supervised information or impose sparsity and
-              linearity, which may not capture the complexity of biological
-              data. We, therefore, introduce Disentangled Representation
-              Variational Inference (DRVI), an unsupervised deep generative
-              model that learns nonlinear, disentangled representations of
-              single-cell omics. This is achieved by combining recently
-              introduced additive decoders with nonlinear pooling, for which we
-              theoretically prove disentanglement under reasonable assumptions.
-              We validate DRVI's disentanglement capabilities across diverse
-              relevant biological problems, from development to perturbational
-              studies and cell atlases, decomposing, for example, the Human Lung
-              Cell Atlas into meaningful, interpretable latent dimensions.
-              Moreover, we demonstrate that if applied to batch integration,
-              DRVI's integration quality does not suffer from the
-              disentanglement constraints and instead is on par with entangled
-              integration methods. With its disentangled latent space, DRVI is
-              inherently interpretable and facilitates the identification of
-              rare cell types, provides novel insights into cellular
-              heterogeneity beyond traditional cell types, and highlights
-              developmental stages.",
   month    =  nov,
   year     =  2024,
   language = "en"