identifying combinations of SNPs that influence the likelihood of a disease state. As part of its method, MDR combines attribute selection and construction (i.e., the creation of a single attribute by pooling data across SNPs) with permutation testing. Like MDR, HotNet, identifies groups of genes related to a disease but does so differently. HotNet uses a diffusion model and a two-stage statistical test to identify groups of mutated/perturbed genes related to a disease. HotNet first formulates an influence measure between pairs of genes using a diffusion process, which is a type of flow problem that has been implemented in protein function prediction on protein interaction networks with significant success (Vandin et al., 2011). Each measure of influence considers a gene to influence another gene if (1) they are both close in distance on the network, and (2) there are relatively few paths between them in the network. Second, subnetworks are identified using an enhanced influence model, in which the number of mutations (e.g., alleles leading to altered protein function) in a gene weights the influence between pairs of genes. Overall, these combinatorial approaches, coupled with the increasing accessibility of GWAS and NGS data across multiple domains provide the means to
