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Protein folding is recognized as a critical problem in  the field of biophysics in the 21st century. Predicting protein-folding  patterns is challenging due to the complex structure of proteins. In an attempt  to solve this problem, we employed ensemble classifiers to improve prediction  accuracy. In our experiments, 188-dimensional features were extracted based on  the composition and physical-chemical property of proteins and 20-dimensional  features were selected using a coupled position-specific scoring matrix.  Compared with traditional prediction methods, these methods were superior in  terms of prediction accuracy. The 188-dimensional feature-based method achieved  71.2% accuracy in five cross-validations. The accuracy rose to 77% when we used  a 20-dimensional feature vector. These methods were used on recent data, with  54.2% accuracy. Source codes and dataset, together with web server and software  tools for prediction, are available at:  http://datamining.xmu.edu.cn/main/~cwc/ProteinPredict.html.

Improved method for predicting protein fold patterns with ensemble classifiers