Learning interpretable disease self-representations for drug repositioning

Diego Galeano Galeano, Fabrizio Frasca, Guadalupe Gonzalez, Ivan Lapanogov, Kirill Veselkov, Alberto Paccanaro, Michael M. Bronstein

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 the proximity between diseases 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.
Original languageEnglish
Title of host publicationConference on Neural Information Processing Systems (NeurIPS) 2019
Subtitle of host publicationGraph representation Learning Workshop
Publication statusAccepted/In press - 2019

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