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arXiv · 1909.06609

Learning Interpretable Disease Self-Representations for Drug Repositioning

Fabrizio Frasca·Diego Galeano·Guadalupe Gonzalez·Ivan Laponogov·Kirill Veselkov·Alberto Paccanaro·Michael M. Bronstein
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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.

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Fabrizio Frasca, Diego Galeano, Guadalupe Gonzalez, Ivan Laponogov, Kirill Veselkov, Alberto Paccanaro, Michael M. Bronstein. 2019-09-14. Learning Interpretable Disease Self-Representations for Drug Repositioning. https://arxiv.org/abs/1909.06609

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