Predicting Protein-Protein Interactions Using Correlation Coefficient and Principle Component Analysis

摘要：

A new features for predicting protein-protein interaction with neural classification is proposed. Our feature extraction is based on the correlation coefficients of physicochemical properties and the statistical means and standard deviations of five secondary structures, i.e. alpha-helix, beta-sheet, beta-turn, coil, and parallel beta strand. The proposed method is tested with yeast Saccharomyces Cerevisiae proteins. Our result uses fewer features which is 50％ less than the others and achieves 92.15％ accuracy higher than the other others.

关键词： protein-protein interactions physicochemical properties correlation coefficient principle component analysis feed-forward neural network

作者: Putthiporn Thanathamathee Chidchanok Lursinsap

作者单位: Advance Virtual and Intelligent Computing (AVIC) Center Department of Mathematics Chulalongkorn University,Bangkok 10330,Thailand

会议类型: 国际会议

会议名称: The 3rd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2009)(第三届生物信息与生物医学工程国际会议)

会议地点: 北京

会议语种:英文

页码: 1-4

在线出版日期: 2009-06-11（万方平台首次上网日期，不代表论文的发表时间）