Integration of Gene Ontology-based similarities for supporting analysis of protein-protein interaction networks

In recent years there has been a growing trend towards the inclusion of diverse genomic information to support comprehensive large-scale prediction of protein-protein interaction networks. The Gene Ontology (GO) is one such functional knowledge resource, which consists of three hierarchies to describe functional attributes of gene products: Molecular function, biological process, and cellular component. Using Bayesian networks, this paper presents a framework for the probabilistic combination of semantic similarity knowledge extracted from the three GO hierarchies for analysis of protein-protein interaction networks and demonstrates its application in yeast. The results indicate that by integrating information encoded in the GO hierarchies a better result can be achieved in terms of both statistical prediction capability and potential biological relevance.