Learning interpretable disease self-representations for drug repositioning. / Galeano Galeano, Diego; Frasca, Fabrizio; Gonzalez, Guadalupe; Lapanogov, Ivan; Veselkov, Kirill; Paccanaro, Alberto; Bronstein, Michael M.

Conference on Neural Information Processing Systems (NeurIPS) 2019: Graph representation Learning Workshop . 2019.

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
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 34792712