Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: XOmiVAE: an interpretable deep learning model for cancer classification
  using high-dimensional omics data   The lack of explainability is one of the most prominent disadvantages of deep
learning applications in omics. This "black box" problem can undermine the
credibility and limit the practical implementation of biomedical deep learning
models. Here we present XOmiVAE, a variational autoencoder (VAE) based
interpretable deep learning model for cancer classification using
high-dimensional omics data. XOmiVAE is capable of revealing the contribution
of each gene and latent dimension for each classification prediction, and the
correlation between each gene and each latent dimension. It is also
demonstrated that XOmiVAE can explain not only the supervised classification
but the unsupervised clustering results from the deep learning network. To the
best of our knowledge, XOmiVAE is one of the first activation level-based
interpretable deep learning models explaining novel clusters generated by VAE.
The explainable results generated by XOmiVAE were validated by both the
performance of downstream tasks and the biomedical knowledge. In our
experiments, XOmiVAE explanations of deep learning based cancer classification
and clustering aligned with current domain knowledge including biological
annotation and academic literature, which shows great potential for novel
biomedical knowledge discovery from deep learning models.

24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th