Building a knowledge graph to enable precision medicine
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Abstract
Developing personalized diagnostic strategies and targeted treatments requires a deep understanding of disease biology and the ability to dissect the relationship between molecular and genetic factors and their phenotypic consequences. However, such knowledge is fragmented across publications, non-standardized repositories, and evolving ontologies describing various scales of biological organization between genotypes and clinical phenotypes. Here, we present PrimeKG, a multimodal knowledge graph for precision medicine analyses. PrimeKG integrates 20 high-quality resources to describe 17,080 diseases with 4,050,249 relationships representing ten major biological scales, including disease-associated protein perturbations, biological processes and pathways, anatomical and phenotypic scales, and the entire range of approved drugs with their therapeutic action, considerably expanding previous efforts in disease-rooted knowledge graphs. PrimeKG contains an abundance of ‘indications’, ‘contradictions’, and ‘off-label use’ drug-disease edges that lack in other knowledge graphs and can support AI analyses of how drugs affect disease-associated networks. We supplement PrimeKG’s graph structure with language descriptions of clinical guidelines to enable multimodal analyses and provide instructions for continual updates of PrimeKG as new data become available.
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The knowledge graph curation and processing approaches are fully reproducible, and the graph can be automatically updated as new data becomes available.
“To this end, the knowledge graph curation and processing approaches are fully reproducible, and the graph can be automatically updated as new data becomes available88.”
From Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases (2025) · cites this paper as [88]
The ‘PrimeKG is easy to use and update’ section says the curation and processing approaches are fully reproducible and can be adapted as new data becomes available. It provides instructions and a ready-to-run notebook for users to build an updated graph, but does not say the graph updates automatically when new data appears. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe effects of drugs can now be prospectively matched to new indications by systematically analyzing medical knowledge graphs (KGs).
“Owing to technological advances, the effects of drugs can now be prospectively matched to new indications by systematically analyzing medical knowledge graphs (KGs)5,9.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [9]
The gold standard labels for evaluating TxGNN come from a previously curated medical KG consisting of 9,388 indications and 30,675 contraindications.
“The gold standard labels for evaluating such a model come from our previously curated medical KG9 (Fig. 1d and Supplementary Tables 4 and 5), which consists of 9,388 indications and 30,675 contraindications23.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [9]
Existing drug-repurposing AI models focus narrowly on diseases for which some drugs already exist, limiting their clinical utility.
“The clinical utility of drug-repurposing artificial intelligence (AI) models remains limited because these models focus narrowly on diseases for which some drugs already exist.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [9]
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A foundation model for clinician-centered drug repurposing
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“We also envision integrating patient information with medical KGs to provide personalized drug-repurposing predictions.”
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- Authors
- Chandak, P., Huang, K., & Zitnik, M.
- Affiliations
- Harvard University (US)
- Stanford University (US)
- Venue
- Scientific Data
- Published
- 2023-02-02
- Volume
- 10
- Issue
- 1
- Pages
- 67-67
- ISSN
- 2052-4463 (ISSN-L)
- PMC
- PMC9893183
- OpenAlex
- W4318983406
- Cited by
- 522
- References
- 115
- Citations by year
- 2026: 178 · 2025: 175 · 2024: 125 · 2023: 44
- Type
- data paper
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Data (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (2030459, 2033384)
- AstraZeneca
- U.S. Air Force (FA8702‐15‐D‐0001, Contract No. FA8702-15-D-0001, Air Force Contract No. FA8702-15-D-0001)
- Harvard Data Science Initiative, Harvard University
- Topics
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- knowledge graphs, precision medicine, multimodal knowledge graphs, drug-disease relationships, off-label drug use, drug indications, drug contraindications, biological pathways, clinical guidelines, drug repurposing
- MeSH terms
- Humans, Pattern Recognition, Automated, Precision Medicine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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