title: Ensemble classifier for protein fold pattern recognition.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  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.
authors: Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200030, China. lifesci-sjtu@san.rr.com
authors: Hong-Bin
authors: HB
authors: Shen
authors: Kuo-Chen
authors: KC
authors: Chou