Morteza Chalabi Hajkarim1, Eli Upfal2, Fabio Vandin3. 1. 1Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen, Denmark. 2. 2Department of Computer Science, Brown University, Providence, RI USA. 3. 3Department of Information Engineering, University of Padova, Padova, Italy.
Abstract
PROBLEM: We study the problem of identifying differentially mutated subnetworks of a large gene-gene interaction network, that is, subnetworks that display a significant difference in mutation frequency in two sets of cancer samples. We formally define the associated computational problem and show that the problem is NP-hard. ALGORITHM: We propose a novel and efficient algorithm, called DAMOKLE, to identify differentially mutated subnetworks given genome-wide mutation data for two sets of cancer samples. We prove that DAMOKLE identifies subnetworks with statistically significant difference in mutation frequency when the data comes from a reasonable generative model, provided enough samples are available. EXPERIMENTAL RESULTS: We test DAMOKLE on simulated and real data, showing that DAMOKLE does indeed find subnetworks with significant differences in mutation frequency and that it provides novel insights into the molecular mechanisms of the disease not revealed by standard methods.
PROBLEM: We study the problem of identifying differentially mutated subnetworks of a large gene-gene interaction network, that is, subnetworks that display a significant difference in mutation frequency in two sets of cancer samples. We formally define the associated computational problem and show that the problem is NP-hard. ALGORITHM: We propose a novel and efficient algorithm, called DAMOKLE, to identify differentially mutated subnetworks given genome-wide mutation data for two sets of cancer samples. We prove that DAMOKLE identifies subnetworks with statistically significant difference in mutation frequency when the data comes from a reasonable generative model, provided enough samples are available. EXPERIMENTAL RESULTS: We test DAMOKLE on simulated and real data, showing that DAMOKLE does indeed find subnetworks with significant differences in mutation frequency and that it provides novel insights into the molecular mechanisms of the disease not revealed by standard methods.
Authors: Charles J Vaske; Stephen C Benz; J Zachary Sanborn; Dent Earl; Christopher Szeto; Jingchun Zhu; David Haussler; Joshua M Stuart Journal: Bioinformatics Date: 2010-06-15 Impact factor: 6.937
Authors: Marcus T Dittrich; Gunnar W Klau; Andreas Rosenwald; Thomas Dandekar; Tobias Müller Journal: Bioinformatics Date: 2008-07-01 Impact factor: 6.937