Protein-protein interaction inference based on semantic similarity of Gene Ontology terms.
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.
Department of Computer Science, Guangzhou Maritime Institute, Room 803, Building 88, Dashabei Road, Huangpu District, Guangzhou 510725, PR China. Electronic address: 845996912@qq.com.
Shu-Bo
SB
Zhang
Department of Shipping, Guangzhou Marine Institute, Room 205, Shipping Building, Hongshan No. 3 Road, Huangpu District, Guangzhou 510725, PR China. Electronic address: tangqiangrong@hotmail.com.
Qiang-Rong
QR
Tang
D063990:Gene Ontology; D006801:Humans; D000069550:Machine Learning; D058977:Molecular Sequence Annotation; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping; D060388:Support Vector Machine; D015003:Yeasts
Ascending similarity; Descending similarity; Feature integration; Gene Ontology; Protein–protein interaction; Support vector machine