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

被引：0
|
作者：
Thanathamathee, Putthiporn [1 ]
Lursinsap, Chidchanok [1 ]
机构：
[1] Chulalongkorn Univ, Dept Math, Adv Virtual & Intelligent Comp AVIC Ctr, Bangkok 10330, Thailand
来源：
2009 3RD INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICAL ENGINEERING, VOLS 1-11 | 2009年
关键词：
protein-protein interactions; physicochemical properties; correlation coefficient; principle component analysis; feed-forward neural network; GENOMES;
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
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 other's and achieves 92.15% accuracy higher than the other other's.
引用
收藏
页码：732 / +
页数：3
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