Wavelet Support Vector Machine and Particle Swarm Optimizer for Prediction of Protein Structural Class

Author

Chen, Chao ; Zou, Xiao-yong

Author_Institution

Sch. of Traditional Chinese Med., Guangdong Pharm. Univ., Guangzhou, China

fYear

2011

fDate

10-12 May 2011

Firstpage

1

Lastpage

4

Abstract

Determination of protein structural class is a quite meaningful topic in protein science. In this paper a wavelet support vector machine (WSVM) coupled with particle swarm optimizer (PSO) is presented for prediction of protein structural class, which is featured by introducing wavelet as a kernel and using PSO to optimize kernel parameters. As a demonstration, the rigorous jackknife cross-validation test was performed on two working datasets that contain 204 and 1673 proteins, respectively. Our success rates were very satisfying, and the optimal mother wavelet was also determined.

Keywords

particle swarm optimisation; proteins; proteomics; support vector machines; wavelet transforms; PSO; kernel parameter optimization; optimal mother wavelet; particle swarm optimizer; protein science; protein structural class prediction; rigorous jackknife cross validation test; success rates; wavelet support vector machine; Amino acids; Convergence; Feature extraction; Kernel; Particle swarm optimization; Proteins; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Biomedical Engineering, (iCBBE) 2011 5th International Conference on

Conference_Location

Wuhan

ISSN

2151-7614

Print_ISBN

978-1-4244-5088-6

Type

conf

DOI

10.1109/icbbe.2011.5780055

Filename

5780055