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1 juin 2020
- doi: 10.1093/bioinformatics/btaa236
- issn: 1367-4803
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info:eu-repo/semantics/altIdentifier/doi/10.1093/bioinformatics/btaa236
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info:eu-repo/semantics/altIdentifier/pmid/32271874
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info:eu-repo/semantics/altIdentifier/eissn/1367-4811
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info:eu-repo/semantics/altIdentifier/urn/urn:nbn:ch:serval-BIB_2BDD22DB11072
info:eu-repo/semantics/openAccess , CC BY-NC 4.0 , https://creativecommons.org/licenses/by-nc/4.0/
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Algorithms; SoftwareCiter ce document
M. Tomasoni et al., « MONET: a toolbox integrating top-performing methods for network modularization. », Serveur académique Lausannois, ID : 10.1093/bioinformatics/btaa236
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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.