MONET: a toolbox integrating top-performing methods for network modularization.

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Ressource 1Download: MONET.pdf (809.77 [Ko])
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Version: Final published version
License: CC BY-NC 4.0
Secondary document(s)
Download: Supplementary_File_BIOINF-2019-2555.R1.pdf (462.03 [Ko])
State: Public
Version: author
License: Not specified
Serval ID
serval:BIB_2BDD22DB1107
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
MONET: a toolbox integrating top-performing methods for network modularization.
Journal
Bioinformatics
Author(s)
Tomasoni M., Gómez S., Crawford J., Zhang W., Choobdar S., Marbach D., Bergmann S.
ISSN
1367-4811 (Electronic)
ISSN-L
1367-4803
Publication state
Published
Issued date
01/06/2020
Peer-reviewed
Oui
Volume
36
Number
12
Pages
3920-3921
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Abstract
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 biomarkers. To this end, we launched the 'Disease Module Identification (DMI) DREAM Challenge', a community effort to build and evaluate unsupervised molecular network modularization algorithms. Here, we present MONET, a toolbox providing easy and unified access to the three top-performing methods from the DMI DREAM Challenge for the bioinformatics community.
MONET 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/BergmannLab/MONET.git.
Supplementary data are available at Bioinformatics online.
Keywords
Algorithms, Software
Pubmed
Web of science
Open Access
Yes
Create date
19/12/2019 11:45
Last modification date
12/01/2021 7:25
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