An integer linear programming approach for finding deregulated subgraphs in regulatory networks.

ABSTRACT: Deregulation of cell signaling pathways plays a crucial role in the development of tumors. The identification of such pathways requires effective analysis tools that facilitate the interpretation of expression differences. Here, we present a novel and highly efficient method for identifying deregulated subnetworks in a regulatory network. Given a score for each node that measures the degree of deregulation of the corresponding gene or protein, the algorithm computes the heaviest connected subnetwork of a specified size reachable from a designated root node. This root node can be interpreted as a molecular key player responsible for the observed deregulation. To demonstrate the potential of our approach, we analyzed three gene expression data sets. In one scenario, we compared expression pr

SUBMITTER: Backes C 

PROVIDER: S-EPMC3315310 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature