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• W2972892933 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." @default.
• W2972892933 created "2019-09-19" @default.
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• W2972892933 date "2019-10-20" @default.
• W2972892933 modified "2023-09-23" @default.
• W2972892933 title "Learning interpretable disease self-representations for drug repositioning" @default.
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