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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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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. |
format | Online Article Text |
id | pubmed-7320625 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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