Protein–protein interaction inference based on semantic similarity of Gene Ontology terms. (21st July 2016) - Zhang, Shu-Bo; Tang, Qiang-Rong
Abstract: Identifying protein–protein interactions is important in molecular biology. Experimental methods to this issue have their limitations, and computational approaches have attracted more and more attentions from the biological community. The semantic similarity derived from the Gene Ontology (GO) annotation has been regarded as one of the most powerful indicators for protein interaction. However, conventional methods based on GO similarity fail to take advantage of the specificity of GO terms in the ontology graph. We proposed a GO-based method to predict protein–protein interaction by integrating different kinds of similarity measures derived from the intrinsic structure of GO graph. We extended five existing methods to derive the semantic similarity measures from the descending part of two GO terms in the GO graph, then adopted a feature integration strategy to combines both the ascending and the descending similarity scores derived from the three sub-ontologies to construct various kinds of features to characterize each protein pair. Support vector machines (SVM) were employed as discriminate classifiers, and five-fold cross validation experiments were conducted on both human and yeast protein–protein interaction datasets to evaluate the performance of different kinds of integrated features, the experimental results suggest the best performance of the feature that combines information from both the ascending and the descending parts of the three ontologies. Our method is appealing for effective prediction of protein–protein interaction. Graphical abstract: Highlights: A GO-driven method to predict protein–protein interaction. Deriving similarity measure from the lower part of GO graph. Constructing feature vector by combining similarities from both upper and lower parts of the three GO graphs. Constructing feature vector by integrating different similarities of various methods. Integrated features generally outperform than individual feature.