PGxMine: Text mining for curation of PharmGKB
Open accessAbstract
Precision medicine tailors treatment to individuals personal data including differences in their genome. The Pharmacogenomics Knowledgebase (PharmGKB) provides highly curated information on the effect of genetic variation on drug response and side effects for a wide range of drugs. PharmGKB's scientific curators triage, review and annotate a large number of papers each year but the task is challenging. We present the PGxMine resource, a text-mined resource of pharmacogenomic associations from all accessible published literature to assist in the curation of PharmGKB. We developed a supervised machine learning pipeline to extract associations between a variant (DNA and protein changes, star alleles and dbSNP identifiers) and a chemical. PGxMine covers 452 chemicals and 2,426 variants and contains 19,930 mentions of pharmacogenomic associations across 7,170 papers. An evaluation by PharmGKB curators found that 57 of the top 100 associations not found in PharmGKB led to 83 curatable papers and a further 24 associations would likely lead to curatable papers through citations. The results can be viewed at https://pgxmine.pharmgkb.org/ and code can be downloaded at https://github.com/jakelever/pgxmine.
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Machine learning and natural language processing methods allow database curators to retrieve and curate more accurately and efficiently relevant literature than traditional keyword-based PubMed search methods.
“showing that machine learning and natural language processing methods (15,16) allow database curators to retrieve and curate more accurately and efficiently relevant literature, than traditional keyword-based PubMed search methods.”
From LitSuggest: a web-based system for literature recommendation and curation using machine learning (2021) · cites this paper as [16]
Cites
Novel mutant-selective EGFR kinase inhibitors against EGFR T790M
Zhou, W., Ercan, D., Chen, L., Yun, C.-H., Li, D., Capelletti, M., Cortot, A. B., Chirieac, L., Iacob, R. E., Padera, R., et al. (2009) · Nature · doi:10.1038/nature08622 · Cited by 1,013 · see citing works
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An Evidence‐Based Framework for Evaluating Pharmacogenomics Knowledge for Personalized Medicine
Whirl‐Carrillo, M., Huddart, R., Gong, L., Sangkuhl, K., Thorn, C. F., Whaley, R., & Klein, T. E. (2021) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2350 · Cited by 810 · see citing works
Self-citationLitSuggest: a web-based system for literature recommendation and curation using machine learning
Allot, A., Lee, K., Chen, Q., Luo, L., & Lu, Z. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab326 · Cited by 61 · see citing works
“Their effectiveness has been demonstrated in multiple publications (12–14), showing that machine learning and natural language processing methods (15,16) allow database curators to retrieve and cu-rate more accurately and efficiently relevant literature, than traditional keyword-based PubMed search…”
Background
- Authors
- Lever, J., Barbarino, J. M., Gong, L., Huddart, R., Sangkuhl, K., Whaley, R., Whirl-Carrillo, M., Woon, M., Klein, T. E., & Altman, R. B.
- Affiliations
- Stanford University (US)
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
- Stanford Medicine (US)
- ORCID
- Venue
- Biocomputing 2020
- Publisher
- WORLD SCIENTIFIC
- Published
- 2019-11-27
- Volume
- 25
- Pages
- 611-622
- PMC
- PMC6917032
- OpenAlex
- W2991516389
- Cited by
- 16
- References
- 1
- Citations by year
- 2025: 2 · 2024: 1 · 2023: 2 · 2022: 3 · 2021: 4 · 2020: 4
- FWCI
- 8.29
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Pacific Symposium on Biocomputing (PSB) (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, cc-by-sa)
- PubMed Central (acceptedVersion, cc-by)
- Funders
- National Institutes of Health (OT2TR002515)
- National Institute of General Medical Sciences (U01GM061374, R24GM061374)
- U.S. National Library of Medicine (R01LM005652)
- National Center for Advancing Translational Sciences
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- pharmacogenomics, PharmGKB, text mining, genetic variation, drug response, precision medicine, literature curation, variant annotation
- MeSH terms
- Humans, Pharmacogenetics, Computational Biology, Databases, Genetic, Knowledge Bases, Data Mining, 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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