title: Using support vector machines for prediction of protein structural classes based on discrete wavelet transform.
abstract: 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.
authors.affiliation: Institute for Advanced Study and Department of Chemistry, Nanchang University, Nanchang 330031, People's Republic of China. jdqiu@ncu.edu.cn
authors.forename: Jian-Ding
authors.initials: JD
authors.lastname: Qiu
authors.forename: San-Hua
authors.initials: SH
authors.lastname: Luo
authors.forename: Jian-Hua
authors.initials: JH
authors.lastname: Huang
authors.forename: Ru-Ping
authors.initials: RP
authors.lastname: Liang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D057927:Hydrophobic and Hydrophilic Interactions; D008956:Models, Chemical; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins