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

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 estab...

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Detalles Bibliográficos
Autores principales: Tomasoni, Mattia, Gómez, Sergio, Crawford, Jake, Zhang, Weijia, Choobdar, Sarvenaz, Marbach, Daniel, Bergmann, Sven
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7320625/
https://www.ncbi.nlm.nih.gov/pubmed/32271874
http://dx.doi.org/10.1093/bioinformatics/btaa236
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author Tomasoni, Mattia
Gómez, Sergio
Crawford, Jake
Zhang, Weijia
Choobdar, Sarvenaz
Marbach, Daniel
Bergmann, Sven
author_facet Tomasoni, Mattia
Gómez, Sergio
Crawford, Jake
Zhang, Weijia
Choobdar, Sarvenaz
Marbach, Daniel
Bergmann, Sven
author_sort Tomasoni, Mattia
collection PubMed
description 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 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. AVAILABILITY AND IMPLEMENTATION: 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 INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-73206252020-07-01 MONET: a toolbox integrating top-performing methods for network modularization Tomasoni, Mattia Gómez, Sergio Crawford, Jake Zhang, Weijia Choobdar, Sarvenaz Marbach, Daniel Bergmann, Sven Bioinformatics Applications Notes 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 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. AVAILABILITY AND IMPLEMENTATION: 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 INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2020-06-15 2020-04-09 /pmc/articles/PMC7320625/ /pubmed/32271874 http://dx.doi.org/10.1093/bioinformatics/btaa236 Text en © The Author(s) 2020. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Applications Notes
Tomasoni, Mattia
Gómez, Sergio
Crawford, Jake
Zhang, Weijia
Choobdar, Sarvenaz
Marbach, Daniel
Bergmann, Sven
MONET: a toolbox integrating top-performing methods for network modularization
title MONET: a toolbox integrating top-performing methods for network modularization
title_full MONET: a toolbox integrating top-performing methods for network modularization
title_fullStr MONET: a toolbox integrating top-performing methods for network modularization
title_full_unstemmed MONET: a toolbox integrating top-performing methods for network modularization
title_short MONET: a toolbox integrating top-performing methods for network modularization
title_sort monet: a toolbox integrating top-performing methods for network modularization
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7320625/
https://www.ncbi.nlm.nih.gov/pubmed/32271874
http://dx.doi.org/10.1093/bioinformatics/btaa236
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