ISubgraph: integrative genomics for subgroup discovery in hepatocellular carcinoma using graph mining and mixture models.

ABSTRACT: The high tumor heterogeneity makes it very challenging to identify key tumorigenic pathways as therapeutic targets. The integration of multiple omics data is a promising approach to identify driving regulatory networks in patient subgroups. Here, we propose a novel conceptual framework to discover patterns of miRNA-gene networks, observed frequently up- or down-regulated in a group of patients and to use such networks for patient stratification in hepatocellular carcinoma (HCC). We developed an integrative subgraph mining approach, called iSubgraph, and identified altered regulatory networks frequently observed in HCC patients. The miRNA and gene expression profiles were jointly analyzed in a graph structure. We defined a method to transform microarray data into graph representation that e

SUBMITTER: Ozdemir B 

PROVIDER: S-EPMC3817163 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature