Using Support Vector Machines for Prediction of Protein Structural Classes Based on Discrete Wavelet Transform

The prediction of secondary structure is a fundamental and important component in the analytical study of protein structure and functions. How to improve the predictive accuracy of protein structural classification by effectively incorporating the sequence-order effects is an important and challenging, problem. In this study, a new method, in which the support vector machine combines with discrete wavelet transform, is developed to predict the protein structural classes. Its performance is assessed by cross-validation tests. The predicted results show that the proposed approach can remarkably improve the success rates, and might become a useful tool for predicting the other attributes of proteins as well. (C) 2008 Wiley Periodicals, Inc. J Comput Chem 30: 1344-1350, 2009