Improving protein-protein interaction pair ranking with an integrated global association score

NCUIR > College of Electrical Engineering & Computer Science > Department of Computer Science and information Engineering > journal & Dissertation > Item 987654321/106899

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/106899

Title:	Improving protein-protein interaction pair ranking with an integrated global association score
Authors:	蔡宗翰;Tsai, Richard Tzong-Han
Contributors:	資訊電機學院資訊工程學系
Keywords:	Bioinformatics;bioinformatics databases;Computational Biology - methods;Data Mining - methods;Databases, Protein;Humans;Mutual information;Protein engineering;Protein Interaction Maps;Proteins;Proteins - chemistry;Proteins - metabolism;Studies;Text mining
Date:	2012-12-01
Issue Date:	2026-04-23 13:48:11 (UTC+8)
Publisher:	Institute of Electrical and Electronics Engineers Inc.;United States: IEEE
Abstract:	摘要： Protein-protein interaction (PPI) database curation requires text-mining systems that can recognize and normalize interactor genes and return a ranked list of PPI pairs for each article. The order of PPI pairs in this list is essential for ease of curation. Most of the current PPI pair ranking approaches rely on association analysis between the two genes in the pair. However, we propose that ranking an extracted PPI pair by considering both the association between the paired genes and each of those genes' global associations with all other genes mentioned in the paper can provide a more reliable ranked list. In this work, we present a composite interaction score that considers not only the association score between two interactors (pair association score) but also their global association scores. We test three representative data fusion algorithms to estimate this global association score-two Borda-Fuse models and one linear combination model (LCM). The three estimation methods are evaluated using the data set of the BioCreative II.5 Interaction Pair Task (IPT) in terms of area under the interpolated precision/recall curve (AUC iP/R). Our experimental results indicate that using LCM to estimate the global association score can boost the AUC iP/R score from 0.0175 to 0.2396, outperforming the best BioCreative II.5 IPT system. 其他題名： TCBB 其他題名： IEEE/ACM Trans Comput Biol Bioinform 出版者： United States: IEEE 出版日期： 2012-11-01 出處： IEEE/ACM transactions on computational biology and bioinformatics, 2012-11, Vol.9 (6), p.1690-1695 資源來源： IEEE Electronic Library (IEL) 版權： Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Nov/Dec 2012 識別號： ISSN: 1545-5963 識別號： ISSN: 1557-9964 識別號： EISSN: 1557-9964 識別號： DOI: 10.1109/TCBB.2012.99 識別號： PMID: 22848135 識別號： CODEN: ITCBCY
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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