J Biomed Inform - Automatic construction of a large-scale and accurate drug-side-effect association knowledge base from biomedical literature.

Tópicos

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{ age(1611) year(1155) adult(843) }
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Resumo

Systems approaches to studying drug-side-effect (drug-SE) associations are emerging as an active research area for drug target discovery, drug repositioning, and drug toxicity prediction. However, currently available drug-SE association databases are far from being complete. Herein, in an effort to increase the data completeness of current drug-SE relationship resources, we present an automatic learning approach to accurately extract drug-SE pairs from the vast amount of published biomedical literature, a rich knowledge source of side effect information for commercial, experimental, and even failed drugs. For the text corpus, we used 119,085,682 MEDLINE sentences and their parse trees. We used known drug-SE associations derived from US Food and Drug Administration (FDA) drug labels as prior knowledge to find relevant sentences and parse trees. We extracted syntactic patterns associated with drug-SE pairs from the resulting set of parse trees. We developed pattern-ranking algorithms to prioritize drug-SE-specific patterns. We then selected a set of patterns with both high precisions and recalls in order to extract drug-SE pairs from the entire MEDLINE. In total, we extracted 38,871 drug-SE pairs from MEDLINE using the learned patterns, the majority of which have not been captured in FDA drug labels to date. On average, our knowledge-driven pattern-learning approach in extracting drug-SE pairs from MEDLINE has achieved a precision of 0.833, a recall of 0.407, and an F1 of 0.545. We compared our approach to a support vector machine (SVM)-based machine learning and a co-occurrence statistics-based approach. We show that the pattern-learning approach is largely complementary to the SVM- and co-occurrence-based approaches with significantly higher precision and F1 but lower recall. We demonstrated by correlation analysis that the extracted drug side effects correlate positively with both drug targets, metabolism, and indications.

Resumo Limpo

system approach studi drugsideeffect drugs associ emerg activ research area drug target discoveri drug reposit drug toxic predict howev current avail drugs associ databas far complet herein effort increas data complet current drugs relationship resourc present automat learn approach accur extract drugs pair vast amount publish biomed literatur rich knowledg sourc side effect inform commerci experiment even fail drug text corpus use medlin sentenc pars tree use known drugs associ deriv us food drug administr fda drug label prior knowledg find relev sentenc pars tree extract syntact pattern associ drugs pair result set pars tree develop patternrank algorithm priorit drugsespecif pattern select set pattern high precis recal order extract drugs pair entir medlin total extract drugs pair medlin use learn pattern major captur fda drug label date averag knowledgedriven patternlearn approach extract drugs pair medlin achiev precis recal f compar approach support vector machin svmbase machin learn cooccurr statisticsbas approach show patternlearn approach larg complementari svm cooccurrencebas approach signific higher precis f lower recal demonstr correl analysi extract drug side effect correl posit drug target metabol indic

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