Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using ontology embeddings for structural inductive bias in gene
  expression data analysis   Stratifying cancer patients based on their gene expression levels allows
improving diagnosis, survival analysis and treatment planning. However, such
data is extremely highly dimensional as it contains expression values for over
20000 genes per patient, and the number of samples in the datasets is low. To
deal with such settings, we propose to incorporate prior biological knowledge
about genes from ontologies into the machine learning system for the task of
patient classification given their gene expression data. We use ontology
embeddings that capture the semantic similarities between the genes to direct a
Graph Convolutional Network, and therefore sparsify the network connections. We
show this approach provides an advantage for predicting clinical targets from
high-dimensional low-sample data.

6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
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The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con