Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/32526
Title: PairProSVM : protein subcellular localization based on local pairwise profile alignment and SVM
Authors: Mak, MW 
Guo, J
Kung, SY
Keywords: Kernel methods
Profile alignment
Protein subcellular localization
Sequence alignment
Support vector machines
Issue Date: 2008
Publisher: ACM Special Interest Group
Source: IEEE/ACM transactions on computational biology and bioinformatics, 2008, v. 5, no. 3, 4384576, p. 416-422 How to cite?
Journal: IEEE/ACM transactions on computational biology and bioinformatics 
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. PairProSVM was evaluated on Huang and Li's and Gardy et al.'s protein data sets. The overall accuracies on these data sets reach 75.3 percent and 91.9 percent, respectively, which are higher than or comparable to those obtained by sequence alignment and composition-based methods.
URI: http://hdl.handle.net/10397/32526
ISSN: 1545-5963
EISSN: 1557-9964
DOI: 10.1109/TCBB.2007.70256
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