title: Sequence-based prediction of protein-protein interactions by means of rotation forest and autocorrelation descriptor.
abstract: We propose a sequence-based multiple classifier system, i.e., rotation forest, to infer protein-protein interactions (PPIs). Moreover, Moran autocorrelation descriptor is used to code an interaction protein pair. Experimental results on Saccharomyces cerevisiae and Helicobacter pylori datasets show that our approach outperforms those previously published in literature, which demonstrates the effectiveness of the proposed method.
authors.affiliation: Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, P.O. Box 1130, Hefei, Anhui 230031, China.
authors.forename: Jun-Feng
authors.initials: JF
authors.lastname: Xia
authors.forename: Kyungsook
authors.initials: K
authors.lastname: Han
authors.forename: De-Shuang
authors.initials: DS
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D001426:Bacterial Proteins / Q000737:chemistry*; D030562:Databases, Protein; D016480:Helicobacter pylori; D010363:Pattern Recognition, Automated / Q000379:methods*; D025941:Protein Interaction Mapping / Q000379:methods*; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry*; D012680:Sensitivity and Specificity; D017421:Sequence Analysis / Q000379:methods*
chemical_list: D001426:Bacterial Proteins; D029701:Saccharomyces cerevisiae Proteins