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Cited by 666Computational Drug Discovery MethodsBiomedical Text Mining and OntologiesBioinformatics and Genomic Networks

Systematic integration of biomedical knowledge prioritizes drugs for repurposing

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Himmelstein, Daniel Scott, Lizee, Antoine, Hessler, Christine, Brueggeman, Leo, Chen, Sabrina L, Hadley, Dexter, Green, Ari, Khankhanian, Pouya +1 more·2017·eLife
DOI 10.7554/elife.26726PMC PMC5640425

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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 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.

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  • SPOKE's reliability has been demonstrated through its successful application in various biomedical contexts.

    “Thus, while SPOKE’s reliability has been demonstrated through its successful application in various biomedical contexts (Supplementary S1 Text; Himmelstein and Baranzini, 2015, Himmelstein et al. 2017, Nelson et al. 2019, Nelson et al. 2021a,b, Baranzini et al. 2022, Morris et al. 2023, Soman et al. 2023a,b, Tang et al. 2024)”

    From Biomedical knowledge graph-optimized prompt generation for large language models (2024) · cites this paper as Himmelstein et al. 2017

    The Introduction and Results > Hetionet v1.0 describe Hetionet and its use in drug repurposing and prototype biomedical queries, not SPOKE. Searches for SPOKE and its reliability found no passage in our copy addressing whether SPOKE was successfully applied across biomedical contexts; page numbers were unavailable. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Himmelstein, D. S., Lizee, A., Hessler, C., Brueggeman, L., Chen, S. L., Hadley, D., Green, A., Khankhanian, P., & Baranzini, S. E.
Affiliations
  • University of California, San Francisco (US)
  • Department of Neurology, University of California, San Francisco, San Francisco, United States
  • University of Iowa (US)
  • Johns Hopkins University (US)
ORCID
Venue
eLife
Publisher
eLife Sciences Publications, Ltd
Published
2017-09-22
Volume
6
ISSN
2050-084X (ISSN-L)
DOI
10.7554/elife.26726
PMC
PMC5640425
OpenAlex
W2949311246
Cited by
666
References
14
Citations by year
2026: 139 · 2025: 110 · 2024: 101 · 2023: 73 · 2022: 84 · 2021: 60 · 2020: 41 · 2019: 39 · 2018: 16 · 2017: 2 · 2016: 1
FWCI
20.11
Type
journal article
Language
en
Open access
Licence
Locations
  • eLife (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • HAL (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
Funders
  • National Science Foundation (1144247)
  • Heidrich Family and Friends Foundation
  • National Institutes of Health (5R01NS088155)
  • National Cancer Institute (UH2CA203792)
  • U.S. National Library of Medicine (1U01LM012675)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
drug repurposing, drug efficacy prediction, biomedical knowledge integration, knowledge graphs, network patterns, epilepsy
MeSH terms
Humans, Models, Biological, Computational Biology, Systems Biology, Drug Discovery, Drug Repositioning

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