ArticleJournal of biomedical informatics2012
Using PharmGKB to train text mining approaches for identifying potential gene targets for pharmacogenomic studies.
Article in Journal of biomedical informatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 21 citations in OpenAlex.
- Text Mining Protocol to Retrieve Significant Drug-Gene Interactions from PubMed Abstracts.Methods in molecular biology (Clifton, N.J.) · 2022Article
- PharmGKB, an Integrated Resource of Pharmacogenomic Knowledge.Current protocols · 2021Article
- PGxCorpus, a manually annotated corpus for pharmacogenomics.Scientific data · 2020Article
- eGARD: Extracting associations between genomic anomalies and drug responses from text.PloS one · 2017Article
- Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research.BMC bioinformatics · 2015Article
- Drug target inference through pathway analysis of genomics data.Advanced drug delivery reviews · 2013Review
- Discovery of novel biomarkers and phenotypes by semantic technologies.BMC bioinformatics · 2013Article
- A Framework of Knowledge Integration and Discovery for Supporting Pharmacogenomics Target Predication of Adverse Drug Events: A Case Study of Drug-Induced Long QT Syndrome.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2013Article
- Extracting Concepts for Precision Oncology from the Biomedical Literature.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational ScienceArticle
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
The main objective of this study was to investigate the feasibility of using PharmGKB, a pharmacogenomic database, as a source of training data in combination with text of MEDLINE abstracts for a text mining approach to identification of potential gene targets for pathway-driven pharmacogenomics research. We used the manually curated relations between drugs and genes in PharmGKB database to train a support vector machine predictive model and applied this model prospectively to MEDLINE abstracts. The gene targets suggested by this approach were subsequently manually reviewed. Our quantitative analysis showed that a support vector machine classifiers trained on MEDLINE abstracts with single words (unigrams) used as features and PharmGKB relations used for supervision, achieve an overall sensitivity of 85% and specificity of 69%. The subsequent qualitative analysis showed that gene targets "suggested" by the automatic classifier were not anticipated by expert reviewers but were subsequently found to be relevant to the three drugs that were investigated: carbamazepine, lamivudine and zidovudine. Our results show that this approach is not only feasible but may also find new gene targets not identifiable by other methods thus making it a valuable tool for pathway-driven pharmacogenomics research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.