J Biomed Inform - Using PharmGKB to train text mining approaches for identifying potential gene targets for pharmacogenomic studies.

Tópicos

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Resumo

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.

Resumo Limpo

main object studi investig feasibl use pharmgkb pharmacogenom databas sourc train data combin text medlin abstract text mine approach identif potenti gene target pathwaydriven pharmacogenom research use manual curat relat drug gene pharmgkb databas train support vector machin predict model appli model prospect medlin abstract gene target suggest approach subsequ manual review quantit analysi show support vector machin classifi train medlin abstract singl word unigram use featur pharmgkb relat use supervis achiev overal sensit specif subsequ qualit analysi show gene target suggest automat classifi anticip expert review subsequ found relev three drug investig carbamazepin lamivudin zidovudin result show approach feasibl may also find new gene target identifi method thus make valuabl tool pathwaydriven pharmacogenom research

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