Learning Interpretable Disease Self-Representations for Drug Repositioning
Abstract
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.
Cite
@article{arxiv.1909.06609,
title = {Learning Interpretable Disease Self-Representations for Drug Repositioning},
author = {Fabrizio Frasca and Diego Galeano and Guadalupe Gonzalez and Ivan Laponogov and Kirill Veselkov and Alberto Paccanaro and Michael M. Bronstein},
journal= {arXiv preprint arXiv:1909.06609},
year = {2019}
}
Comments
10 pages, 2 figures, v2 corresponds to the camera ready version accepted at the Graph Representation Learning Workshop, NeurIPS 2019