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Elife. 2017 Sep 22;6. pii: e26726. doi: 10.7554/eLife.26726.

Systematic integration of biomedical knowledge prioritizes drugs for repurposing.

Author information

1
Biological and Medical Informatics Program, University of California, San Francisco, San Francisco, United States.
2
Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, United States.
3
Department of Neurology, University of California, San Francisco, San Francisco, United States.
4
ITUN-CRTI-UMR 1064 Inserm, University of Nantes, Nantes, France.
5
University of Iowa, Iowa City, United States.
6
Johns Hopkins University, Baltimore, United States.
7
Department of Pediatrics, University of California, San Fransisco, San Fransisco, United States.
8
Institute for Computational Health Sciences, University of California, San Francisco, San Francisco, United States.
9
Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, United States.

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 were 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.

KEYWORDS:

computational biology; drug repurposing; heterogeneous networks; human; machine learning; systems biology

PMID:
28936969
PMCID:
PMC5640425
DOI:
10.7554/eLife.26726
[Indexed for MEDLINE]
Free PMC Article

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