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TCBB
2008

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

13 years 2 months ago
PairProSVM: Protein Subcellular Localization Based on Local Pairwise Profile Alignment and SVM
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
Man-Wai Mak, Jian Guo, Sun-Yuan Kung
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Where TCBB
Authors Man-Wai Mak, Jian Guo, Sun-Yuan Kung
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