RT Journal Article
SR Electronic
T1 <em>DreamDMI</em>: a tool for Disease Module Identification in molecular networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 611418
DO 10.1101/611418
A1 Mattia Tomasoni
A1 Sarvenaz Choobdar
A1 Daniel Marbach
A1 Sergio Gómez
A1 Jake Crawford
A1 Weijia Zhang
A1 Sven Bergmann
YR 2019
UL http://biorxiv.org/content/early/2019/05/07/611418.abstract
AB Summary We define a disease module as a partition of a molecular network whose components are jointly associated with one or several diseases or risk factors thereof. Identification of such modules, across different types of networks, has great potential for elucidating disease mechanisms and establishing new powerful bio-markers. To this end, we launched the “Disease Module Identification (DMI) DREAM Challenge”, a community effort to build and evaluate unsupervised molecular network modularisation algorithms (Choobdar et al., 2018). Here we present DreamDMI, a toolbox providing easy and unified access to the three top methods from the DMI DREAM Challenge for the bioinformatics community.Availability and Implementation DreamDMI is a command line tool for Linux, based on Docker and Singularity containers; the core algorithms were written in R, Python, Ada and C++. It is freely available for download at https://github.com/mattiat/DREAM_DMI_ToolContact mattia.tomasoni{at}unil.ch