title: Prediction of interacting protein pairs from sequence using a Bayesian method.
abstract: With the development of bioinformatics, more and more protein sequence information has become available. Meanwhile, the number of known protein-protein interactions (PPIs) is still very limited. In this article, we propose a new method for predicting interacting protein pairs using a Bayesian method based on a new feature representation. We trained our model using data on 6,459 PPI pairs from the yeast Saccharomyces cerevisiae core subset. Using six species of DIP database, our model demonstrates an average prediction accuracy of 93.67%. The result showed that our method is superior to other methods in both computing time and prediction accuracy.
authors.affiliation: Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, AnHui University, 230039, Hefei, China. wyxcs@163.com
authors.forename: Chishe
authors.initials: C
authors.lastname: Wang
authors.forename: Jiaxing
authors.initials: J
authors.lastname: Cheng
authors.forename: Shoubao
authors.initials: S
authors.lastname: Su
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D019540:Area Under Curve; D001499:Bayes Theorem; D055598:Chemical Phenomena; D030562:Databases, Protein; D006801:Humans; D011485:Protein Binding; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping; D011506:Proteins / Q000737:chemistry*; D012372:ROC Curve; D015203:Reproducibility of Results; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins; D029701:Saccharomyces cerevisiae Proteins