NoMAS: A Computational Approach to Find Mutated Subnetworks Associated With Survival in Genome-Wide Cancer Studies

Discussion In this work, we study the problem of identifying subnetworks of a large gene-gene interaction network that are associated with survival using mutations from large cancer genomic studies. Few methods have been proposed to identify groups of genes with mutations associated with survival in genomic studies. The work of Vandin et al. (2012a) combines mutations and survival data with interaction information using a diffusion process on graphs starting from gene scores derived from p-values of individual genes, but did not consider the problem of directly identifying groups of genes associated with survival. The work of Reimand and Bader (2013) combines mutation information and patient survival to identify subnetworks of a kinase-substrate interaction network associated with survival. It only focuses on phosphorylation-associated mutations, and the approach is based on a local search algorithm that builds a subnetwork by starting from one seed vertex and then greedily adding neighbors (at distance at most 2) from the seed, extending the approach used in different types of network analyses (Chuang et al., 2007). A similar greedy approach is used by Wu and Stein (2012) to identify groups of genes significantly associated with survival in cancer from gene expression data. For gene expression studies, Chowdhury et al. (2011) proposes an approach to enumerate dysregulated subnetworks in cancer based on an efficient search space pruning strategy, inspired by previous work on...
Source: Frontiers in Genetics - Category: Genetics & Stem Cells Source Type: research