Protein cellular localization prediction with Support Vector Machines and Decision Trees
Computers in Biology and Medicine
Subcellular Localization Prediction with New Protein Encoding Schemes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Multiclass multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Using pseudo amino acid composition to predict protein subnuclear localization: Approached with PSSM
Pattern Recognition Letters
Computational Biology and Chemistry
An Automated Combination of Kernels for Predicting Protein Subcellular Localization
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
g-MARS: Protein Classification Using Gapped Markov Chains and Support Vector Machines
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Class Prediction from Disparate Biological Data Sources Using an Iterative Multi-Kernel Algorithm
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Predicting protein subcellular locations for Gram-negative bacteria using neural networks ensemble
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Classifying proteins using gapped Markov feature pairs
Neurocomputing
Robust prediction of protein subcellular localization combining PCA and WSVMs
Computers in Biology and Medicine
Transactions on Computational Systems Biology II
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Motivation: PSORTb v.1.1 is the most precise bacterial localization prediction tool available. However, the program's predictive coverage and recall are low and the method is only applicable to Gram-negative bacteria. The goals of the present work are as follows: increase PSORTb's coverage while maintaining the existing precision level, expand it to include Gram-positive bacteria and then carry out a comparative analysis of localization. Results: An expanded database of proteins of known localization and new modules using frequent subsequence-based support vector machines was introduced into PSORTb v.2.0. The program attains a precision of 96% for Gram-positive and Gram-negative bacteria and predictive coverage comparable to other tools for whole proteome analysis. We show that the proportion of proteins at each localization is remarkably consistent across species, even in species with varying proteome size. Availability: Web-based version: http://www.psort.org/psortb. Standalone version: Available through the website under GNU General Public License. Contact:psort-mail@sfu.ca, brinkman@sfu.ca Supplementary information: http://www.psort.org/psortb/supplementaryinfo.html