title: Inferring protein interactions from experimental data by association probabilistic method.
abstract: To elucidate protein interaction networks is one of the major goals of functional genomics for whole organisms. So far, various computational methods have been proposed for inference of protein-protein interactions. Based on the association method by Sprinzak et al., we propose an association probabilistic method in this short communication to infer protein interactions directly from the experimental data, which outperformed other existing methods in terms of both accuracy and efficiency despite its simple form. Specifically, we show that the association probabilistic method achieves the highest accuracy among the existing approaches for the measures of root-mean-square error and the Pearson correlation coefficient, and also runs much faster than the LP-based method, by experimental dataset in Yeast. Software is available from the authors upon request.
authors.affiliation: Osaka Sangyo University, Osaka, Japan. chen@elec.osaka-sandai.ac.jp
authors.forename: Luonan
authors.initials: L
authors.lastname: Chen
authors.forename: Ling-Yun
authors.initials: LY
authors.lastname: Wu
authors.forename: Yong
authors.initials: Y
authors.lastname: Wang
authors.forename: Xiang-Sun
authors.initials: XS
authors.lastname: Zhang
mesh_terms: D030562:Databases, Protein; D016678:Genome; D008962:Models, Theoretical; D011336:Probability; D011506:Proteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism*; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae
chemical_list: D011506:Proteins