title: Predicting protein-protein interactions from protein sequences using meta predictor.
abstract: A novel method is proposed for predicting protein-protein interactions (PPIs) based on the meta approach, which predicts PPIs using support vector machine that combines results by six independent state-of-the-art predictors. Significant improvement in prediction performance is observed, when performed on Saccharomyces cerevisiae and Helicobacter pylori datasets. In addition, we used the final prediction model trained on the PPIs dataset of S. cerevisiae to predict interactions in other species. The results reveal that our meta model is also capable of performing cross-species predictions. The source code and the datasets are available at http://home.ustc.edu.cn/~jfxia/Meta_PPI.html.
authors.affiliation: Intelligent Computing Laboratory, 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: Xing-Ming
authors.initials: XM
authors.lastname: Zhao
authors.forename: De-Shuang
authors.initials: DS
authors.lastname: Huang
mesh_terms: D001426:Bacterial Proteins / Q000737:chemistry* / Q000378:metabolism*; D016208:Databases, Factual; D016480:Helicobacter pylori / Q000737:chemistry; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000737:chemistry; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry* / Q000378:metabolism*
chemical_list: D001426:Bacterial Proteins; D029701:Saccharomyces cerevisiae Proteins