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PGxMine: Text mining for curation of PharmGKB
Jake Lever1, Julia M Barbarino, Li Gong
1Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 5, 2019
Summary
PGxMine is a new text-mined resource that identifies pharmacogenomic associations between genetic variants and drug responses. This tool aids the curation of the Pharmacogenomics Knowledgebase (PharmGKB), improving precision medicine research.
Area of Science:
- Pharmacogenomics
- Bioinformatics
- Computational Biology
Background:
- Precision medicine utilizes individual genetic data to tailor drug treatments.
- The Pharmacogenomics Knowledgebase (PharmGKB) curates genetic variations' effects on drug response.
- Manual curation of scientific literature for pharmacogenomic data is labor-intensive.
Purpose of the Study:
- To develop PGxMine, a text-mined resource to assist PharmGKB curation.
- To extract pharmacogenomic associations between genetic variants and chemicals from published literature.
Main Methods:
- A supervised machine learning pipeline was developed.
- The pipeline extracts associations between variants (DNA/protein changes, star alleles, dbSNP IDs) and chemicals.
- The resource covers 452 chemicals, 2,426 variants, and 19,930 associations from 7,170 papers.
Main Results:
- PGxMine identified numerous pharmacogenomic associations.
- Evaluation by PharmGKB curators indicated that 57 of the top 100 novel associations led to 83 curatable papers.
- An additional 24 associations were likely to yield curatable papers via citations.
Conclusions:
- PGxMine effectively extracts pharmacogenomic associations from scientific literature.
- The resource significantly aids the PharmGKB curation process.
- PGxMine enhances the discovery of genotype-drug-response relationships for precision medicine.
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