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PIPE: a protein–protein interaction passage extraction module for BioCreative challenge

Identifying the interactions between proteins mentioned in biomedical literatures is one of the frequently discussed topics of text mining in the life science field. In this article, we propose PIPE, an interaction pattern generation module used in the Collaborative Biocurator Assistant Task at BioC...

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Autores principales: Chang, Yung-Chun, Chu, Chun-Han, Su, Yu-Chen, Chen, Chien Chin, Hsu, Wen-Lian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4983456/
https://www.ncbi.nlm.nih.gov/pubmed/27524807
http://dx.doi.org/10.1093/database/baw101
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author Chang, Yung-Chun
Chu, Chun-Han
Su, Yu-Chen
Chen, Chien Chin
Hsu, Wen-Lian
author_facet Chang, Yung-Chun
Chu, Chun-Han
Su, Yu-Chen
Chen, Chien Chin
Hsu, Wen-Lian
author_sort Chang, Yung-Chun
collection PubMed
description Identifying the interactions between proteins mentioned in biomedical literatures is one of the frequently discussed topics of text mining in the life science field. In this article, we propose PIPE, an interaction pattern generation module used in the Collaborative Biocurator Assistant Task at BioCreative V (http://www.biocreative.org/) to capture frequent protein-protein interaction (PPI) patterns within text. We also present an interaction pattern tree (IPT) kernel method that integrates the PPI patterns with convolution tree kernel (CTK) to extract PPIs. Methods were evaluated on LLL, IEPA, HPRD50, AIMed and BioInfer corpora using cross-validation, cross-learning and cross-corpus evaluation. Empirical evaluations demonstrate that our method is effective and outperforms several well-known PPI extraction methods. Database URL:
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spelling pubmed-49834562016-08-17 PIPE: a protein–protein interaction passage extraction module for BioCreative challenge Chang, Yung-Chun Chu, Chun-Han Su, Yu-Chen Chen, Chien Chin Hsu, Wen-Lian Database (Oxford) Original Article Identifying the interactions between proteins mentioned in biomedical literatures is one of the frequently discussed topics of text mining in the life science field. In this article, we propose PIPE, an interaction pattern generation module used in the Collaborative Biocurator Assistant Task at BioCreative V (http://www.biocreative.org/) to capture frequent protein-protein interaction (PPI) patterns within text. We also present an interaction pattern tree (IPT) kernel method that integrates the PPI patterns with convolution tree kernel (CTK) to extract PPIs. Methods were evaluated on LLL, IEPA, HPRD50, AIMed and BioInfer corpora using cross-validation, cross-learning and cross-corpus evaluation. Empirical evaluations demonstrate that our method is effective and outperforms several well-known PPI extraction methods. Database URL: Oxford University Press 2016-08-14 /pmc/articles/PMC4983456/ /pubmed/27524807 http://dx.doi.org/10.1093/database/baw101 Text en © The Author(s) 2016. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Chang, Yung-Chun
Chu, Chun-Han
Su, Yu-Chen
Chen, Chien Chin
Hsu, Wen-Lian
PIPE: a protein–protein interaction passage extraction module for BioCreative challenge
title PIPE: a protein–protein interaction passage extraction module for BioCreative challenge
title_full PIPE: a protein–protein interaction passage extraction module for BioCreative challenge
title_fullStr PIPE: a protein–protein interaction passage extraction module for BioCreative challenge
title_full_unstemmed PIPE: a protein–protein interaction passage extraction module for BioCreative challenge
title_short PIPE: a protein–protein interaction passage extraction module for BioCreative challenge
title_sort pipe: a protein–protein interaction passage extraction module for biocreative challenge
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4983456/
https://www.ncbi.nlm.nih.gov/pubmed/27524807
http://dx.doi.org/10.1093/database/baw101
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