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Systematic integration of biomedical knowledge prioritizes drugs for repurposing

Daniel S Himmelstein, Antoine Lizee, Christine Hessler, Leo Brueggeman, Sabrina L Chen, Dexter Hadley, Ari Green, Pouya Khankhanian, Sergio E Baranzini
doi: https://doi.org/10.1101/087619
Daniel S Himmelstein
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Antoine Lizee
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Christine Hessler
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Leo Brueggeman
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Sabrina L Chen
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Dexter Hadley
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Ari Green
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Pouya Khankhanian
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Sergio E Baranzini
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Abstract

The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes the Rephetio Project to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (https://neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data was integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then we predicted the probability of treatment for 209,168 compound–;disease pairs (http://het.io/repurpose). Our predictions validated in two external datasets, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 36 community members.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted November 14, 2016.
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Systematic integration of biomedical knowledge prioritizes drugs for repurposing
Daniel S Himmelstein, Antoine Lizee, Christine Hessler, Leo Brueggeman, Sabrina L Chen, Dexter Hadley, Ari Green, Pouya Khankhanian, Sergio E Baranzini
bioRxiv 087619; doi: https://doi.org/10.1101/087619
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Systematic integration of biomedical knowledge prioritizes drugs for repurposing
Daniel S Himmelstein, Antoine Lizee, Christine Hessler, Leo Brueggeman, Sabrina L Chen, Dexter Hadley, Ari Green, Pouya Khankhanian, Sergio E Baranzini
bioRxiv 087619; doi: https://doi.org/10.1101/087619

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