title: Prediction of protein-protein interactions based on PseAA composition and hybrid feature selection.
abstract: 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.
authors.affiliation: Department of Chemistry, College of Sciences, Shanghai University, 99 Shang-Da Road, Shanghai 200444, People's Republic of China.
authors.forename: Liang
authors.initials: L
authors.lastname: Liu
authors.forename: Yudong
authors.initials: Y
authors.lastname: Cai
authors.forename: Wencong
authors.initials: W
authors.lastname: Lu
authors.forename: Kaiyan
authors.initials: K
authors.lastname: Feng
authors.forename: Chunrong
authors.initials: C
authors.lastname: Peng
authors.forename: Bing
authors.initials: B
authors.lastname: Niu
mesh_terms: 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
chemical_list: D000596:Amino Acids; D011506:Proteins