Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Interpretable Disease Self-Representations for Drug
  Repositioning   Drug repositioning is an attractive cost-efficient strategy for the
development of treatments for human diseases. Here, we propose an interpretable
model that learns disease self-representations for drug repositioning. Our
self-representation model represents each disease as a linear combination of a
few other diseases. We enforce proximity in the learnt representations in a way
to preserve the geometric structure of the human phenome network - a
domain-specific knowledge that naturally adds relational inductive bias to the
disease self-representations. We prove that our method is globally optimal and
show results outperforming state-of-the-art drug repositioning approaches. We
further show that the disease self-representations are biologically
interpretable.

11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
3
The categories of this paper are: physics