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Drug repositioning is an attractive cost-efficient strategy for thedevelopment of treatments for human diseases. Here, we propose an interpretablemodel that learns disease self-representations for drug repositioning. Ourself-representation model represents each disease as a linear combination of afew other diseases. We enforce proximity in the learnt representations in a wayto preserve the geometric structure of the human phenome network - adomain-specific knowledge that naturally adds relational inductive bias to thedisease self-representations. We prove that our method is globally optimal andshow results outperforming state-of-the-art drug repositioning approaches. Wefurther show that the disease self-representations are biologicallyinterpretable.

Learning Interpretable Disease Self-Representations for Drug Repositioning