Bioinformatics Advance Access originally published online on May 3, 2006
Bioinformatics 2006 22(14):1717-1722; doi:10.1093/bioinformatics/btl170
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Ensemble classifier for protein fold pattern recognition
1 Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University Shanghai 200030, China
2 Gordon Life Science Institute San Diego, CA 92130, USA
*To whom correspondence should be addressed.
Motivation: Prediction of protein folding patterns is one level deeper than that of protein structural classes, and hence is much more complicated and difficult. To deal with such a challenging problem, the ensemble classifier was introduced. It was formed by a set of basic classifiers, with each trained in different parameter systems, such as predicted secondary structure, hydrophobicity, van der Waals volume, polarity, polarizability, as well as different dimensions of pseudo-amino acid composition, which were extracted from a training dataset. The operation engine for the constituent individual classifiers was OET-KNN (optimized evidence-theoretic k-nearest neighbors) rule. Their outcomes were combined through a weighted voting to give a final determination for classifying a query protein. The recognition was to find the true fold among the 27 possible patterns.
Results: The overall success rate thus obtained was 62% for a testing dataset where most of the proteins have <25% sequence identity with the proteins used in training the classifier. Such a rate is 6–21% higher than the corresponding rates obtained by various existing NN (neural networks) and SVM (support vector machines) approaches, implying that the ensemble classifier is very promising and might become a useful vehicle in protein science, as well as proteomics and bioinformatics.
Availability: The ensemble classifier, called PFP-Pred, is available as a web-server at http://202.120.37.186/bioinf/fold/PFP-Pred.htm for public usage.
Contact: lifesci-sjtu{at}san.rr.com
Supplementary information: Supplementary data are available on Bioinformatics online.
Received on March 31, 2006; revised on April 26, 2006; accepted on April 27, 2006
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