Inferring semantic similarity through correlating information contents of gene ontology terms

Successful applications of the gene ontology to infer functional relationships between gene products in recent years have raised the need for computational methods to automatically calculate semantic similarity between gene products based on the gene ontology. To meet this challenge, several methods have been proposed to derive semantic similarity between gene products based on semantic similarity of gene ontology terms. However, these methods, though having been widely used in a variety of applications, may significantly overestimate semantic similarity between genes that are actually not functional related, thereby yielding misleading results in applications. To overcome this limitation, we propose to represent a gene product as a vector that is composed of information contents of gene ontology terms annotated for the gene product. Results show that semantic similarity scores calculated using our proposed method are more consistent with known biological knowledge than those derived using a list of existing methods, suggesting the effectiveness of our method in characterizing functional relationships between gene products.