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.
Qiu
Jian-Ding
JD
Institute for Advanced Study and Department of Chemistry, Nanchang University, Nanchang 330031, People's Republic of China. jdqiu@ncu.edu.cn
Luo
San-Hua
SH
Huang
Jian-Hua
JH
Liang
Ru-Ping
RP
D000465:Algorithms; D003198:Computer Simulation; D057927:Hydrophobic and Hydrophilic Interactions; D008956:Models, Chemical; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*
D011506:Proteins