A Bayesian approach to construct Context-Specific Gene Ontology: Application to protein function prediction

The annotation of protein provides a considerable knowledge for the biologists in order to understand life at the molecular level. The computational annotation of protein function has therefore emerged as an important alternative given that the biological experiments are extremely laborious. A number of methods have been developed to computationally annotate proteins using standardized nomenclatures such as Gene Ontology. These methods are based on various independency assumptions for modeling the annotation problem. However, the recent network analysis reveals that the same protein with different interactions may perform different functions. In this paper, we take into account the topology of the protein-protein interaction network in order to propose a new representation of functions' ontology. We use the Bayesian network in order to model and to alter the structure of this ontology so as to create the new context specific ontology. We use this newly proposed structure for predicting the functions of the unlabeled proteins. We evaluate our method, called Context-Specific Ontology by the use of the Bayesian Network (ConSOn-BN), on the Saccharomyces cerevisiae protein-protein interaction network and we find that ConSOn-BN has enhanced results as compared to some known methods.