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 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

References in zbMATH (referenced in 5 articles )

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  1. Mei, Suyu: \textitSVMensemble based transfer learning for large-scale membrane proteins discrimination (2014)
  2. Mei, S.; Wang, F.; Zhou, S.: Gene ontology based transfer learning for protein subcellular localization (2011) ioport
  3. Zakeri, Pooya; Moshiri, Behzad; Sadeghi, Mehdi: Prediction of protein submitochondria locations based on data fusion of various features of sequences (2011)
  4. Mei, Suyu; Fei, Wang: Amino acid classification based spectrum kernel fusion for protein subnuclear localization (2010) ioport
  5. Mak, Man-Wai; Guo, Jian; Kung, Sun-Yuan: Pairprosvm: Protein subcellular localization based on local pairwise profile alignment and SVM (2008) ioport