Protein domain annotation with integration of heterogeneous information sources

Abstract

Domains are structural and functional units of proteins and play an important role in functional genomics. Theoretically, the functions of a protein can be directly inferred if the biological functions of its component domains are determined. Despite the important role that domains play, only a small number of domains have been annotated so far, and few works have been performed to predict the functions of domains. Hence, it is necessary to develop automatic methods for predicting domain functions based on various available data. In this article, two new methods, that is, the threshold-based classification method and the support vector machines method, are proposed for protein domain function prediction by integrating heterogeneous information sources, including protein-domain mapping features, domain-domain interactions, and domain coexisting features. We show that the integration of heterogeneous information sources improves not only prediction accuracy but also annotation reliability when compared with the methods using only individual information sources.