Prediction of protein-protein interactions based on PseAA composition and hybrid feature selection.
Based on pseudo amino acid (PseAA) composition and a novel hybrid feature selection frame, this paper presents a computational system to predict the PPIs (protein-protein interactions) using 8796 protein pairs. These pairs are coded by PseAA composition, resulting in 114 features. A hybrid feature selection system, mRMR-KNNs-wrapper, is applied to obtain an optimized feature set by excluding poor-performed and/or redundant features, resulting in 103 remaining features. Using the optimized 103-feature subset, a prediction model is trained and tested in the k-nearest neighbors (KNNs) learning system. This prediction model achieves an overall accurate prediction rate of 76.18%, evaluated by 10-fold cross-validation test, which is 1.46% higher than using the initial 114 features and is 6.51% higher than the 20 features, coded by amino acid compositions. The PPIs predictor, developed for this research, is available for public use at http://chemdata.shu.edu.cn/ppi.
Liu
Liang
L
Department of Chemistry, College of Sciences, Shanghai University, 99 Shang-Da Road, Shanghai 200444, People's Republic of China.
Cai
Yudong
Y
Lu
Wencong
W
Feng
Kaiyan
K
Peng
Chunrong
C
Niu
Bing
B
D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry* / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
D000596:Amino Acids; D011506:Proteins