Measure the Semantic Similarity of GO Terms Using Aggregate Information Content

Abstract

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. This paper proposes a novel and efficient method to measure the semantic similarity of GO terms. This method addresses the limitations in existing GO term similarity measurement methods by using the information content of all ancestor terms of a GO term to determine the GO term’s semantic content. The aggregate information content 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 outperforms all existing methods in terms of the correlation with gene expression data.