Measure the Semantic Similarity of GO Terms Using Aggregate Information Content.

The rapid development of gene ontology (GO) and huge amount of biomedical data annotated by GO terms necessitate computation of semantic similarity of GO terms and, in turn, measurement of functional similarity of genes based on their annotations. In this paper we propose a novel and efficient method to measure the semantic similarity of GO terms. The proposed method addresses the limitations in existing GO term similarity measurement techniques; it computes the semantic content of a GO term by considering the information content of all of its ancestor terms in the graph. The aggregate information content (AIC) of all ancestor terms of a GO term implicitly reflects the GO term's location in the GO graph and also represents how human beings use this GO term and all its ancestor terms to annotate genes. We show that semantic similarity of GO terms obtained by our method closely matches the human perception. Extensive experimental studies show that this novel method also outperforms all existing methods in terms of the correlation with gene expression data. We have developed web services for measuring semantic similarity of GO terms and functional similarity of genes using the proposed AIC method and other popular methods. These web services are available at http://bioinformatics.clemson.edu/G-SESAME.