Evaluating topology-based metrics for GO term similarity measures

Defining semantic functional similarity measures provides effective means to validate protein function prediction methods and to retrieve biologically relevant information from big biological data. It also improves understanding of interrelationship between genes and gene products (GPs). Currently, one of the most commonly used tools for functionally annotating genes and GPs is the Gene Ontology (GO), which describes genes/GPs using a machine-readable language. To measure the semantic similarity between two GO terms, many studies that are based on GO topology have recently been reported. However, a comprehensive assessment and general guidelines for validating these methods are lacking. In this paper, we collect a large dataset to evaluate five often-used semantic similarity measure methods by estimating sequence similarity, phylogenetic profile similarity, and structural similarity. We further compare the measures in terms of their clustering performance using domains extracted from SCOP database. We describe some key aspects of these measure methods and discuss how the limitations may be addressed as well as some open problems.