PPI-IRO: A two-stage method for protein-protein interaction extraction based on interaction relation ontology

Chuanxi Li; Peng Chen; Rujing Wang; Xiujie Wang; Yaru Su; Jinyan Li

doi:10.1504/IJDMB.2014.062890

PPI-IRO: A two-stage method for protein-protein interaction extraction based on interaction relation ontology

Chuanxi Li, Peng Chen, Rujing Wang, Xiujie Wang, Yaru Su, Jinyan Li

Computer, Electrical and Mathematical Sciences and Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

Mining Protein-Protein Interactions (PPIs) from the fast-growing biomedical literature resources has been proven as an effective approach for the identifi cation of biological regulatory networks. This paper presents a novel method based on the idea of Interaction Relation Ontology (IRO), which specifi es and organises words of various proteins interaction relationships. Our method is a two-stage PPI extraction method. At fi rst, IRO is applied in a binary classifi er to determine whether sentences contain a relation or not. Then, IRO is taken to guide PPI extraction by building sentence dependency parse tree. Comprehensive and quantitative evaluations and detailed analyses are used to demonstrate the signifi cant performance of IRO on relation sentences classifi cation and PPI extraction. Our PPI extraction method yielded a recall of around 80% and 90% and an F1 of around 54% and 66% on corpora of AIMed and Bioinfer, respectively, which are superior to most existing extraction methods. Copyright © 2014 Inderscience Enterprises Ltd.

Original language	English (US)
Pages (from-to)	98
Journal	International Journal of Data Mining and Bioinformatics
Volume	10
Issue number	1
DOIs	https://doi.org/10.1504/IJDMB.2014.062890
State	Published - 2014

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We thank the anonymous reviewers for their constructive comments on the paper. This research was supported in part by the National Natural Science Foundation of China (No. 60774096) and the National Key Technology R&D Program of China (No. 2008BAK49B05). This work was also supported in party by the National Science Foundation of China (No. 60803107).

ASJC Scopus subject areas

General Biochemistry, Genetics and Molecular Biology
Library and Information Sciences
Information Systems

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10.1504/IJDMB.2014.062890

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@article{7299b35a4a2d4cdb9bad153d09d441a5,

title = "PPI-IRO: A two-stage method for protein-protein interaction extraction based on interaction relation ontology",

abstract = "Mining Protein-Protein Interactions (PPIs) from the fast-growing biomedical literature resources has been proven as an effective approach for the identifi cation of biological regulatory networks. This paper presents a novel method based on the idea of Interaction Relation Ontology (IRO), which specifi es and organises words of various proteins interaction relationships. Our method is a two-stage PPI extraction method. At fi rst, IRO is applied in a binary classifi er to determine whether sentences contain a relation or not. Then, IRO is taken to guide PPI extraction by building sentence dependency parse tree. Comprehensive and quantitative evaluations and detailed analyses are used to demonstrate the signifi cant performance of IRO on relation sentences classifi cation and PPI extraction. Our PPI extraction method yielded a recall of around 80% and 90% and an F1 of around 54% and 66% on corpora of AIMed and Bioinfer, respectively, which are superior to most existing extraction methods. Copyright {\textcopyright} 2014 Inderscience Enterprises Ltd.",

author = "Chuanxi Li and Peng Chen and Rujing Wang and Xiujie Wang and Yaru Su and Jinyan Li",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: We thank the anonymous reviewers for their constructive comments on the paper. This research was supported in part by the National Natural Science Foundation of China (No. 60774096) and the National Key Technology R&D Program of China (No. 2008BAK49B05). This work was also supported in party by the National Science Foundation of China (No. 60803107).",

year = "2014",

doi = "10.1504/IJDMB.2014.062890",

language = "English (US)",

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AU - Li, Chuanxi

AU - Chen, Peng

AU - Wang, Rujing

AU - Wang, Xiujie

AU - Su, Yaru

AU - Li, Jinyan

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: We thank the anonymous reviewers for their constructive comments on the paper. This research was supported in part by the National Natural Science Foundation of China (No. 60774096) and the National Key Technology R&D Program of China (No. 2008BAK49B05). This work was also supported in party by the National Science Foundation of China (No. 60803107).

PY - 2014

Y1 - 2014

N2 - Mining Protein-Protein Interactions (PPIs) from the fast-growing biomedical literature resources has been proven as an effective approach for the identifi cation of biological regulatory networks. This paper presents a novel method based on the idea of Interaction Relation Ontology (IRO), which specifi es and organises words of various proteins interaction relationships. Our method is a two-stage PPI extraction method. At fi rst, IRO is applied in a binary classifi er to determine whether sentences contain a relation or not. Then, IRO is taken to guide PPI extraction by building sentence dependency parse tree. Comprehensive and quantitative evaluations and detailed analyses are used to demonstrate the signifi cant performance of IRO on relation sentences classifi cation and PPI extraction. Our PPI extraction method yielded a recall of around 80% and 90% and an F1 of around 54% and 66% on corpora of AIMed and Bioinfer, respectively, which are superior to most existing extraction methods. Copyright © 2014 Inderscience Enterprises Ltd.

AB - Mining Protein-Protein Interactions (PPIs) from the fast-growing biomedical literature resources has been proven as an effective approach for the identifi cation of biological regulatory networks. This paper presents a novel method based on the idea of Interaction Relation Ontology (IRO), which specifi es and organises words of various proteins interaction relationships. Our method is a two-stage PPI extraction method. At fi rst, IRO is applied in a binary classifi er to determine whether sentences contain a relation or not. Then, IRO is taken to guide PPI extraction by building sentence dependency parse tree. Comprehensive and quantitative evaluations and detailed analyses are used to demonstrate the signifi cant performance of IRO on relation sentences classifi cation and PPI extraction. Our PPI extraction method yielded a recall of around 80% and 90% and an F1 of around 54% and 66% on corpora of AIMed and Bioinfer, respectively, which are superior to most existing extraction methods. Copyright © 2014 Inderscience Enterprises Ltd.

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PPI-IRO: A two-stage method for protein-protein interaction extraction based on interaction relation ontology

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