Abstract
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.
| Original language | English |
|---|---|
| Journal | Database : the journal of biological databases and curation |
| Volume | 2016 |
| DOIs | |
| Publication status | Published - Jan 1 2016 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Information Systems
- Biochemistry, Genetics and Molecular Biology(all)
- Agricultural and Biological Sciences(all)
Cite this
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.
In: Database : the journal of biological databases and curation, Vol. 2016, 01.01.2016.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - PIPE
T2 - a protein-protein interaction passage extraction module for BioCreative challenge
AU - Chang, Yung Chun
AU - Chu, Chun Han
AU - Su, Yu Chen
AU - Chen, Chien Chin
AU - Hsu, Wen Lian
PY - 2016/1/1
Y1 - 2016/1/1
N2 - 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.
AB - 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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UR - http://www.scopus.com/inward/citedby.url?scp=85032923304&partnerID=8YFLogxK
U2 - 10.1093/database/baw101
DO - 10.1093/database/baw101
M3 - Article
C2 - 27524807
AN - SCOPUS:85032923304
VL - 2016
JO - Database : the journal of biological databases and curation
JF - Database : the journal of biological databases and curation
SN - 1758-0463
ER -