Systematic integration of biomedical knowledge prioritizes drugs for repurposing
Systematic integration of biomedical knowledge prioritizes drugs for repurposing
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.
Baranzini Sergio E.、Himmelstein Daniel S.、Hessler Christine、Brueggeman Leo、Chen Sabrina L.、Khankhanian Pouya、Lizee Antoine、Green Ari、Hadley Dexter
Program in Biological & Medical Informatics, University of California||Department of Neurology, University of CaliforniaProgram in Biological & Medical Informatics, University of California||Department of Systems Pharmacology & Translational Therapeutics, University of PennsylvaniaDepartment of Neurology, University of CaliforniaDepartment of Neurology, University of California||University of IowaDepartment of Neurology, University of California||Johns Hopkins UniversityDepartment of Neurology, University of California||Center for Neuroengineering and Therapeutics, University of PennsylvaniaDepartment of Neurology, University of California||ITUN-CRTI-UMR 1064 Inserm, University of Nantes.Department of Neurology, University of CaliforniaInstitute for Computational Health Sciences, Department of Pediatrics. University of California
医药卫生理论医学研究方法药学
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Baranzini Sergio E.,Himmelstein Daniel S.,Hessler Christine,Brueggeman Leo,Chen Sabrina L.,Khankhanian Pouya,Lizee Antoine,Green Ari,Hadley Dexter.Systematic integration of biomedical knowledge prioritizes drugs for repurposing[EB/OL].(2025-03-28)[2025-05-15].https://www.biorxiv.org/content/10.1101/087619.点此复制
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