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The Gene Ontology (GO) provides a knowledge base to effectively describeproteins. However, measuring similarity between proteins based on GO remains achallenge. In this paper, we propose a new similarity measure, informationcoefficient similarity measure (SimIC), to effectively integrate both theinformation content (IC) of GO terms and the structural information of GOhierarchy to determine the similarity between proteins. Testing on yeastproteins, our results show that SimIC efficiently addresses the shallowannotation issue in GO, thus improves the correlations between GO similaritiesof yeast proteins and their expression similarities as well as between GOsimilarities of yeast proteins and their sequence similarities. Furthermore, wedemonstrate that the proposed SimIC is superior in predicting yeast proteininteractions. We predict 20484 yeast protein-protein interactions (PPIs)between 2462 proteins based on the high SimIC values of biological process (BP)and cellular component (CC). Examining the 214 MIPS complexes in our predictedPPIs shows that all members of 159 MIPS complexes can be found in our PPIpredictions, which is more than those (120/214) found in PPIs predicted byrelative specificity similarity (RSS). Integrating IC and structuralinformation of GO hierarchy can improve the effectiveness of the semanticsimilarity measure of GO terms. The new SimIC can effectively correct theeffect of shallow annotation, and then provide an effective way to measuresimilarity between proteins based on Gene Ontology.

Effectively integrating information content and structural relationship to improve the GO-based similarity measure between proteins