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Systematic integration of biomedical knowledge prioritizes drugs for repurposing

View ORCID ProfileDaniel S. Himmelstein, View ORCID ProfileAntoine Lizee, Christine Hessler, View ORCID ProfileLeo Brueggeman, Sabrina L. Chen, View ORCID ProfileDexter Hadley, View ORCID ProfileAri Green, View ORCID ProfilePouya Khankhanian, View ORCID ProfileSergio E. Baranzini
doi: https://doi.org/10.1101/087619
Daniel S. Himmelstein
1Program in Biological & Medical Informatics, University of California, San Francisco; Department of Systems Pharmacology & Translational Therapeutics, University of Pennsylvania
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Antoine Lizee
2Department of Neurology, University of California, San Francisco; ITUN-CRTI-UMR 1064 Inserm, University of Nantes
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Christine Hessler
3Department of Neurology, University of California, San Francisco
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Leo Brueggeman
4Department of Neurology, University of California, San Francisco; University of Iowa
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Sabrina L. Chen
5Department of Neurology, University of California, San Francisco; Johns Hopkins University
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Dexter Hadley
6Institute for Computational Health Sciences, Department of Pediatrics; University of California, San Francisco
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Ari Green
7Department of Neurology, University of California, San Francisco
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Pouya Khankhanian
8Department of Neurology, University of California, San Francisco; Center for Neuroengineering and Therapeutics, University of Pennsylvania
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Sergio E. Baranzini
9Department of Neurology, University of California, San Francisco
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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 Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (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 (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 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 August 31, 2017.
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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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