Prioritizing Type 2 Diabetes Genes by Weighted PageRank on Bilayer Heterogeneous Networks

The prevalence of diabetes mellitus has been increasing rapidly in recent years. Type 2 diabetes makes up about 90 percent cases of diabetes. The interacting mixed effects of genetics and environments build possible interpretable pathogenesis. Thus, finding the causal disease genes is crucial in its clinical diagnosis and medical treatment. Currently, network-based computational method becomes a powerful tool of systematically analyzing complex diseases, such as the identification of candidate disease genes from networks. In this paper, we propose a bioinformatics framework of prioritizing type 2 diabetes genes by leveraging the modified PageRank algorithm on bilayer biomolecular networks consisting an ensemble gene-gene regulatory network and an integrative protein-protein interaction network. We specifically weigh the networks by differential mutual information for measuring the context specificities between genes and between proteins by transcriptomic and proteomic datasets, respectively. After formulating the network into two components of known disease genes and the other normal healthy genes, we rank the diabetes genes and others by bringing the orders in the bilayer network via an improved PageRank algorithm. We conclude that these known disease genes achieve significantly higher ranks compared to these randomly-selected normal genes, and the ranks are robust and consistent in multiple validation scenarios. In functional analysis, these high-ranked genes are identified to perform relevant risks and dysfunctions of type 2 diabetes.