Subcellular Localization Prediction with New Protein Encoding Schemes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
PairProSVM: Protein Subcellular Localization Based on Local Pairwise Profile Alignment and SVM
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Robust prediction of protein subcellular localization combining PCA and WSVMs
Computers in Biology and Medicine
Feature subset selection for protein subcellular localization prediction
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
| Hi-index | 3.84 |
Summary: We developed a web server PSLpred for predicting subcellular localization of gram-negative bacterial proteins with an overall accuracy of 91.2%. PSLpred is a hybrid approach-based method that integrates PSI-BLAST and three SVM modules based on compositions of residues, dipeptides and physico-chemical properties. The prediction accuracies of 90.7, 86.8, 90.3, 95.2 and 90.6% were attained for cytoplasmic, extracellular, inner-membrane, outer-membrane and periplasmic proteins, respectively. Furthermore, PSLpred was able to predict ∼74% of sequences with an average prediction accuracy of 98% at RI = 5. Availability: PSLpred is available at http://www.imtech.res.in/raghava/pslpred/ Contact: raghava@imtech.res.in Supplementary information: http://www.imtech.res.in/raghava/pslpred/supl.html