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Building a knowledge graph to enable precision medicine

Paper · 2023 · Payal Chandak, Kexin Huang and Marinka Žitnik

Summary

Knowledge about diseases is fragmented across publications, databases and ontologies that encode diseases in different and often conflicting ways. This makes systematic, large-scale study of disease biology difficult for precision medicine. The authors built PrimeKG, a disease-centred knowledge graph that merges 20 curated resources, including DisGeNET, MONDO, Bgee, DrugBank, CTD and the Mayo Clinic knowledgebase. It links diseases to proteins, pathways, biological processes, anatomy, phenotypes, environmental exposures and drugs.

Mismatched disease vocabularies were a major obstacle. To address this, the team embedded MONDO terms with the language model ClinicalBERT and clustered them by cosine similarity, producing groupings closer to the disease subtypes clinicians actually use. Drug and disease nodes also carry textual clinical descriptions, so the graph supports both structured and text-based analyses.

PrimeKG covers one to two orders of magnitude more diseases than earlier disease-focused graphs. It is especially rich in drug–disease therapeutic edges. In a retrospective test, drugs given new indications after the graph was built were usually located near their target diseases, which suggests real value for drug repurposing.

Key points

  • Scale: 129,375 nodes, 4,050,249 relationships and 17,080 diseases across ten biological scales, drawn from 20 primary resources. For comparison, SPOKE covers only 137 diseases.
  • PrimeKG contains many indication, contraindication and off-label use drug–disease edges, which are rare or absent in other knowledge graphs.
  • Autism shows the entity-resolution problem: clinicians recognise 3 subtypes, while UMLS lists 192, MONDO 37 and Orphanet 6. ClinicalBERT-based clustering condensed the MONDO terms into 16 groupings.
  • Repurposing check: the authors took 11 drugs given new FDA indications after assembly, only one of which already had an indication edge in the graph. In 8 of the 10 testable pairs, the drug was significantly closer to its disease than random diseases (Bonferroni-adjusted P ≤ 0.05).
  • The graph is available on Harvard Dataverse (doi:10.7910/DVN/IXA7BM), with code and update scripts on GitHub (mims-harvard/PrimeKG).

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksComputational Theory and Mathematics · 252 worksBiochemistry, Genetics and Molecular Biology · 234 worksMolecular Biology · 231 works

🧭 Topics 1Research subjects and languages the work is about.Knowledge graphs & ontologies · 192 works

💡 Concepts 1Key ideas and methods explained on our concept pages.Knowledge graph · 49 works

Cite

Cite as Payal Chandak, Kexin Huang and Marinka Žitnik. 2023. Building a knowledge graph to enable precision medicine. Scientific Data.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1038/s41597-023-01960-3

@inproceedings{chandak2023building,
  title = {{Building a knowledge graph to enable precision medicine}},
  author = {Payal Chandak, Kexin Huang and Marinka Žitnik},
  booktitle = {Scientific Data},
  year = {2023},
  doi = {10.1038/s41597-023-01960-3},
  url = {https://doi.org/10.1038/s41597-023-01960-3}
}

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