Integrative Protein Function Transfer Using Factor Graphs and Heterogeneous Data Sources
We propose a novel approach for predicting protein functions of an organism by coupling sequence homology and PPI data between two (or more) species with multi-functional Gene Ontology information into a single computational model. Instead of using a network of one organism in isolation, we join networks of different species by inter-species sequence homology links of sufficient similarity. As a consequence, the knowledge of a protein's function is acquired not only from one species' network alone, but also through homologous links to the networks of different species. We apply our method to two largest protein networks, Yeast (Saccharomyces cerevisiae) and Fly (Drosophila melanogaster). Our joint Fly-Yeast network displays statistically significant improvements in precision, accuracy, and false positive rate over networks that consider either of the sources in isolation, while retaining the computational efficiency of the simpler models.