Systematic integration of biomedical knowledge prioritizes drugs for repurposing
University of California, San Francisco · Translational Therapeutics (United States) · +6 more institutions
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…
Citation impact
- FWCI
- 25.83
- Percentile
- 100%
- References
- 221
Authors
9- DHDaniel HimmelsteinCorresponding
University of California, San Francisco, Translational Therapeutics (United States), University of Pennsylvania
- ALAntoine Lizée
Inserm, University of California, San Francisco, Nantes Université
- CHChristine Hessler
University of California, San Francisco
- LBLeo Brueggeman
University of Iowa, University of California, San Francisco, United States University
- SCSabrina Chen
Johns Hopkins University, University of California, San Francisco
Topics & keywords
- Repurposing
- Drug repositioning
- Disease
- Computer science
- Drug
- Computational biology
- Drug discovery
- Systems biology