Extraction of Pharmacokinetic Evidence of Drug-drug Interactions from the Literature
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.7/400 |
Resumo: | 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. ... |
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Extraction of Pharmacokinetic Evidence of Drug-drug Interactions from the LiteratureStatistics - Machine LearningComputer Science - Information RetrievalQuantitative Biology - Quantitative MethodsDrug-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. ...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.PLOSARCAKolchinsky, ArtemyLourenço, AnáliaWu, Heng-YiLi, LangRocha, Luis M.2015-10-14T13:49:31Z2015-05-112015-05-11T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.7/400engKolchinsky 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.012219910.1371/journal.pone.0122199info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-11-29T14:34:47Zoai:arca.igc.gulbenkian.pt:10400.7/400Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:11:41.955878Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Extraction of Pharmacokinetic Evidence of Drug-drug Interactions from the Literature |
title |
Extraction of Pharmacokinetic Evidence of Drug-drug Interactions from the Literature |
spellingShingle |
Extraction of Pharmacokinetic Evidence of Drug-drug Interactions from the Literature Kolchinsky, Artemy Statistics - Machine Learning Computer Science - Information Retrieval Quantitative Biology - Quantitative Methods |
title_short |
Extraction of Pharmacokinetic Evidence of Drug-drug Interactions from the Literature |
title_full |
Extraction of Pharmacokinetic Evidence of Drug-drug Interactions from the Literature |
title_fullStr |
Extraction of Pharmacokinetic Evidence of Drug-drug Interactions from the Literature |
title_full_unstemmed |
Extraction of Pharmacokinetic Evidence of Drug-drug Interactions from the Literature |
title_sort |
Extraction of Pharmacokinetic Evidence of Drug-drug Interactions from the Literature |
author |
Kolchinsky, Artemy |
author_facet |
Kolchinsky, Artemy Lourenço, Anália Wu, Heng-Yi Li, Lang Rocha, Luis M. |
author_role |
author |
author2 |
Lourenço, Anália Wu, Heng-Yi Li, Lang Rocha, Luis M. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
ARCA |
dc.contributor.author.fl_str_mv |
Kolchinsky, Artemy Lourenço, Anália Wu, Heng-Yi Li, Lang Rocha, Luis M. |
dc.subject.por.fl_str_mv |
Statistics - Machine Learning Computer Science - Information Retrieval Quantitative Biology - Quantitative Methods |
topic |
Statistics - Machine Learning Computer Science - Information Retrieval Quantitative Biology - Quantitative Methods |
description |
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. ... |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-10-14T13:49:31Z 2015-05-11 2015-05-11T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.7/400 |
url |
http://hdl.handle.net/10400.7/400 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
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 10.1371/journal.pone.0122199 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
PLOS |
publisher.none.fl_str_mv |
PLOS |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799130572241502208 |