PPCM: Combing Multiple Classifiers to Improve Protein-Protein Interaction Prediction.

ABSTRACT: Determining protein-protein interaction (PPI) in biological systems is of considerable importance, and prediction of PPI has become a popular research area. Although different classifiers have been developed for PPI prediction, no single classifier seems to be able to predict PPI with high confidence. We postulated that by combining individual classifiers the accuracy of PPI prediction could be improved. We developed a method called protein-protein interaction prediction classifiers merger (PPCM), and this method combines output from two PPI prediction tools, GO2PPI and Phyloprof, using Random Forests algorithm. The performance of PPCM was tested by area under the curve (AUC) using an assembled Gold Standard database that contains both positive and negative PPI pairs. Our AUC test showed t

SUBMITTER: Yao J 

PROVIDER: S-EPMC4619929 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature