Extraction of Pharmacokinetic Evidence of Drug-drug Interactions from  the Literature

Kolchinsky, Artemy; Lourenço, Anália; Wu, Heng-Yi; Li, Lang; Rocha, Luis M.

Publication

Extraction of Pharmacokinetic Evidence of Drug-drug Interactions from the Literature

2015-05-11Journal article

dc.contributor.author	Kolchinsky, Artemy
dc.contributor.author	Lourenço, Anália
dc.contributor.author	Wu, Heng-Yi
dc.contributor.author	Li, Lang
dc.contributor.author	Rocha, Luis M.
dc.date.accessioned	2015-10-14T13:49:31Z
dc.date.available	2015-10-14T13:49:31Z
dc.date.issued	2015-05-11
dc.description.abstract	Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases. Though DDI is investigated in domains ranging in scale from intracellular biochemistry to human populations, literature mining has not been used to extract specific types of experimental evidence, which are reported differently for distinct experimental goals. We focus on pharmacokinetic evidence for DDI, essential for identifying causal mechanisms of putative interactions and as input for further pharmacological and pharmaco-epidemiology investigations. We used manually curated corpora of PubMed abstracts and annotated sentences to evaluate the efficacy of literature mining on two tasks: first, identifying PubMed abstracts containing pharmacokinetic evidence of DDIs; second, extracting sentences containing such evidence from abstracts. We implemented a text mining pipeline and evaluated it using several linear classifiers and a variety of feature transforms. The most important textual features in the abstract and sentence classification tasks were analyzed. We also investigated the performance benefits of using features derived from PubMed metadata fields, various publicly available named entity recognizers, and pharmacokinetic dictionaries. Several classifiers performed very well in distinguishing relevant and irrelevant abstracts (reaching F1~=0.93, MCC~=0.74, iAUC~=0.99) and sentences (F1~=0.76, MCC~=0.65, iAUC~=0.83). We found that word bigram features were important for achieving optimal classifier performance and that features derived from Medical Subject Headings (MeSH) terms significantly improved abstract classification. ...	pt_PT
dc.description.sponsorship	National Institutes of Health, National Library of Medicine Program grant: (01LM011945-01 "BLR: Evidence-based Drug-Interaction Discovery: In-Vivo, In-Vitro and Clinical), Indiana University Collaborative Research Program 2013 grant, Fundação Luso-Americana para o Desenvolvimento (Portugal) and National Science Foundation (USA) 2012-2014 grant.	pt_PT
dc.identifier	10.1371/journal.pone.0122199
dc.identifier.citation	Kolchinsky A, Lourenço A, Wu H-Y, Li L, Rocha LM (2015) Extraction of Pharmacokinetic Evidence of Drug – Drug Interactions from the Literature. PLoS ONE 10(5): e0122199. doi:10.1371/ journal.pone.0122199	pt_PT
dc.identifier.doi	10.1371/journal.pone.0122199
dc.identifier.uri	http://hdl.handle.net/10400.7/400
dc.language.iso	eng	pt_PT
dc.peerreviewed	yes	pt_PT
dc.publisher	PLOS	pt_PT
dc.relation.publisherversion	http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0122199	pt_PT
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	pt_PT
dc.subject	Statistics - Machine Learning	pt_PT
dc.subject	Computer Science - Information Retrieval	pt_PT
dc.subject	Quantitative Biology - Quantitative Methods	pt_PT
dc.title	Extraction of Pharmacokinetic Evidence of Drug-drug Interactions from the Literature	pt_PT
dc.type	journal article
dspace.entity.type	Publication
oaire.citation.endPage	24	pt_PT
oaire.citation.issue	5	pt_PT
oaire.citation.startPage	1	pt_PT
oaire.citation.title	PLOS One	pt_PT
oaire.citation.volume	10	pt_PT
rcaap.rights	openAccess	pt_PT
rcaap.type	article	pt_PT