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PairProSVM: Protein Subcellular Localization Based on Local Pairwise Profile Alignment and SVM

Authors:

Sun-Yuan KungAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 5, Issue 3

Pages 416 - 422

https://doi.org/10.1109/TCBB.2007.70256

Published: 01 July 2008 Publication History

Abstract

The subcellular locations of proteins are important functional annotations. An effective and reliable subcellular localization method is necessary for proteomics research. This paper introduces a new method---PairProSVM---to automatically predict the subcellular locations of proteins. The profiles of all protein sequences in the training set are constructed by PSI-BLAST and the pairwise profile-alignment scores are used to form feature vectors for training a support vector machine (SVM) classifier. It was found that PairProSVM outperforms the methods that are based on sequence alignment and amino-acid compositions even if most of the homologous sequences have been removed. This paper also demonstrates that the performance of PairProSVM is sensitive (and somewhat proportional) to the degree of its kernel matrix meeting the Mercer's condition. PairProSVM was evaluated on Reinhardt and Hubbard's, Huang and Li's, and Gardy et al.'s protein datasets. The overall accuracies on these three datasets reach 99.3\\%, 76.5\\%, and 91.9\\%, respectively, which are higher than or comparable to those obtained by sequence alignment and by the methods compared in this paper.

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PairProSVM: Protein Subcellular Localization Based on Local Pairwise Profile Alignment and SVM

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cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 5, Issue 3

July 2008

159 pages

ISSN:1545-5963

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IEEE Computer Society Press

Washington, DC, United States

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Published: 01 July 2008

Published in TCBB Volume 5, Issue 3

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Cited By

Wan SMak MKung S(2017)Transductive Learning for Multi-Label Protein Subchloroplast Localization PredictionIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2016.252765714:1(212-224)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1109/TCBB.2016.2527657
Wei LLiao MGao XWang JLin W(2016)mGOF-locNeurocomputing10.1016/j.neucom.2015.09.137217:C(73-82)Online publication date: 12-Dec-2016
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Du NKnecht MSwihart MTang ZWalsh TZhang A(2015)Identifying affinity classes of inorganic materials binding sequences via a graph-based modelIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2014.232115812:1(193-204)Online publication date: 1-Jan-2015
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Du NKnecht MPrasad PSwihart MWalsh TZhang A(2013)A Framework for Identifying Affinity Classes of Inorganic Materials Binding Peptide SequencesProceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics10.1145/2506583.2506628(545-551)Online publication date: 22-Sep-2013
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Tian JGu HLiu WGao C(2011)Robust prediction of protein subcellular localization combining PCA and WSVMsComputers in Biology and Medicine10.1016/j.compbiomed.2011.05.01641:8(648-652)Online publication date: 1-Aug-2011
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