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Published on: December 9, 2015
A mutation-centric approach to identifying pharmacogenomic relations in text
Bastien Rance1, Emily Doughty, Dina Demner-Fushman
1National Library of Medicine, Bethesda, MD 20894, USA.
Journal of Biomedical Informatics
|June 12, 2012
Summary
This study introduces a mutation-centric approach for extracting pharmacogenomic relations from scientific literature, achieving 33-46% recall and 65% precision. This method aids biocurators in identifying drug-gene variant associations.
Area of Science:
- Bioinformatics
- Computational Biology
- Pharmacogenomics
Background:
- Pharmacogenomic relations link genetic variations to drug responses.
- Automated extraction of these relations from literature is crucial for drug development and personalized medicine.
- Existing methods often focus on genes rather than specific mutations.
Purpose of the Study:
- To develop and evaluate a mutation-centric approach for pharmacogenomic relation extraction.
- To compare this approach against a relation-centric method and reference databases.
- To assess the utility of automated tools for biocurators.
Main Methods:
- Utilized MetaMap and RxNorm for drug mention extraction and EMU for genetic variant extraction from MEDLINE abstracts.
- Focused on identifying specific genetic variants, not just gene mentions, using biological knowledge.
- Evaluated recall against manually curated relations in PharmGKB and precision on a random sample.
Main Results:
- The mutation-centric approach achieved 33-46% recall on reference abstracts.
- Applied to a large corpus, it identified pharmacogenomic relations in 4534 abstracts with 65% precision.
- Demonstrated complementary results when compared to a relation-centric approach.
Conclusions:
- The mutation-centric approach shows comparable recall but slightly lower precision than relation-centric methods.
- Both approaches are complementary and can be combined for comprehensive relation extraction.
- These high-throughput methods serve as valuable tools to assist biocurators in identifying pharmacogenomic relations from literature, highlighting challenges in full-text processing and variant resolution.
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