Protein-protein Interaction Passage Extraction Using the Interaction Pattern Kernel Approach for the BioCreative 2015 BioC Track

Yung-Chun Chang, Yu-Chen Su, Chun Han Chu, Chien Chin Chen, Wen Lian Hsu

Research output: Contribution to journalArticle

6 Downloads (Pure)

Abstract

Discovering the interactions between proteins mentioned in biomedi-cal literatures is one of the core topics of text mining in the field of life science. In this paper, we propose a system under interaction pattern generation approach to capture frequent PPI patterns in text with the use of official BioC API and Semantic Class Labeling. We also present an interaction pattern tree kernel method that integrates the PPI pattern with convolution tree kernel to extract protein-protein interactions. Empirical evaluations on the LLL, IEPA, and HPRD50 corpora demonstrate that our method is effective and outper-forms several well-known PPI extraction methods. 1 Introduction With the growing number of research papers, researchers now have difficulty in retrieving those that exactly fulfill their needs. As for life scientists, relationships between entities mentioned in these papers are the major target of interest. Among biomed relation types, protein– protein interaction (PPI) extraction is becoming critical in the field of molecular biology due to demands for automatic discovery of molecular pathways and interactions in the literature.
Original languageEnglish
Pages (from-to)10-16
Number of pages7
JournalBioCreative V workshop
Publication statusPublished - 2015
Externally publishedYes

Fingerprint

Proteins
Molecular biology
Convolution
Application programming interfaces (API)
Labeling
Semantics

Cite this

Protein-protein Interaction Passage Extraction Using the Interaction Pattern Kernel Approach for the BioCreative 2015 BioC Track. / Chang, Yung-Chun; Su, Yu-Chen; Chu, Chun Han; Chen, Chien Chin; Hsu, Wen Lian.

In: BioCreative V workshop, 2015, p. 10-16.

Research output: Contribution to journalArticle

@article{f7f6e7127cc04c228e198ea6e5dd234c,
title = "Protein-protein Interaction Passage Extraction Using the Interaction Pattern Kernel Approach for the BioCreative 2015 BioC Track",
abstract = "Discovering the interactions between proteins mentioned in biomedi-cal literatures is one of the core topics of text mining in the field of life science. In this paper, we propose a system under interaction pattern generation approach to capture frequent PPI patterns in text with the use of official BioC API and Semantic Class Labeling. We also present an interaction pattern tree kernel method that integrates the PPI pattern with convolution tree kernel to extract protein-protein interactions. Empirical evaluations on the LLL, IEPA, and HPRD50 corpora demonstrate that our method is effective and outper-forms several well-known PPI extraction methods. 1 Introduction With the growing number of research papers, researchers now have difficulty in retrieving those that exactly fulfill their needs. As for life scientists, relationships between entities mentioned in these papers are the major target of interest. Among biomed relation types, protein– protein interaction (PPI) extraction is becoming critical in the field of molecular biology due to demands for automatic discovery of molecular pathways and interactions in the literature.",
author = "Yung-Chun Chang and Yu-Chen Su and Chu, {Chun Han} and Chen, {Chien Chin} and Hsu, {Wen Lian}",
year = "2015",
language = "English",
pages = "10--16",
journal = "BioCreative V workshop",

}

TY - JOUR

T1 - Protein-protein Interaction Passage Extraction Using the Interaction Pattern Kernel Approach for the BioCreative 2015 BioC Track

AU - Chang, Yung-Chun

AU - Su, Yu-Chen

AU - Chu, Chun Han

AU - Chen, Chien Chin

AU - Hsu, Wen Lian

PY - 2015

Y1 - 2015

N2 - Discovering the interactions between proteins mentioned in biomedi-cal literatures is one of the core topics of text mining in the field of life science. In this paper, we propose a system under interaction pattern generation approach to capture frequent PPI patterns in text with the use of official BioC API and Semantic Class Labeling. We also present an interaction pattern tree kernel method that integrates the PPI pattern with convolution tree kernel to extract protein-protein interactions. Empirical evaluations on the LLL, IEPA, and HPRD50 corpora demonstrate that our method is effective and outper-forms several well-known PPI extraction methods. 1 Introduction With the growing number of research papers, researchers now have difficulty in retrieving those that exactly fulfill their needs. As for life scientists, relationships between entities mentioned in these papers are the major target of interest. Among biomed relation types, protein– protein interaction (PPI) extraction is becoming critical in the field of molecular biology due to demands for automatic discovery of molecular pathways and interactions in the literature.

AB - Discovering the interactions between proteins mentioned in biomedi-cal literatures is one of the core topics of text mining in the field of life science. In this paper, we propose a system under interaction pattern generation approach to capture frequent PPI patterns in text with the use of official BioC API and Semantic Class Labeling. We also present an interaction pattern tree kernel method that integrates the PPI pattern with convolution tree kernel to extract protein-protein interactions. Empirical evaluations on the LLL, IEPA, and HPRD50 corpora demonstrate that our method is effective and outper-forms several well-known PPI extraction methods. 1 Introduction With the growing number of research papers, researchers now have difficulty in retrieving those that exactly fulfill their needs. As for life scientists, relationships between entities mentioned in these papers are the major target of interest. Among biomed relation types, protein– protein interaction (PPI) extraction is becoming critical in the field of molecular biology due to demands for automatic discovery of molecular pathways and interactions in the literature.

M3 - Article

SP - 10

EP - 16

JO - BioCreative V workshop

JF - BioCreative V workshop

ER -