A multi-objective genetic algorithm to find active modules in multiplex biological networks.

The identification of subnetworks of interest-or active modules-by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular conditions. We here propose MOGAMUN, a Multi-Objective Genetic Algorithm to identify active modules...

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Autores principales:	Elva María Novoa-Del-Toro, Efrén Mezura-Montes, Matthieu Vignes, Morgane Térézol, Frédérique Magdinier, Laurent Tichit, Anaïs Baudot
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/c6d3a18612264ec793ee179ae807abcd
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spelling	oai:doaj.org-article:c6d3a18612264ec793ee179ae807abcd2021-12-02T19:58:00ZA multi-objective genetic algorithm to find active modules in multiplex biological networks.1553-734X1553-735810.1371/journal.pcbi.1009263https://doaj.org/article/c6d3a18612264ec793ee179ae807abcd2021-08-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009263https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The identification of subnetworks of interest-or active modules-by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular conditions. We here propose MOGAMUN, a Multi-Objective Genetic Algorithm to identify active modules in MUltiplex biological Networks. MOGAMUN optimizes both the density of interactions and the scores of the nodes (e.g., their differential expression). We compare MOGAMUN with state-of-the-art methods, representative of different algorithms dedicated to the identification of active modules in single networks. MOGAMUN identifies dense and high-scoring modules that are also easier to interpret. In addition, to our knowledge, MOGAMUN is the first method able to use multiplex networks. Multiplex networks are composed of different layers of physical and functional relationships between genes and proteins. Each layer is associated to its own meaning, topology, and biases; the multiplex framework allows exploiting this diversity of biological networks. We applied MOGAMUN to identify cellular processes perturbed in Facio-Scapulo-Humeral muscular Dystrophy, by integrating RNA-seq expression data with a multiplex biological network. We identified different active modules of interest, thereby providing new angles for investigating the pathomechanisms of this disease. Availability: MOGAMUN is available at https://github.com/elvanov/MOGAMUN and as a Bioconductor package at https://bioconductor.org/packages/release/bioc/html/MOGAMUN.html. Contact: anais.baudot@univ-amu.fr.Elva María Novoa-Del-ToroEfrén Mezura-MontesMatthieu VignesMorgane TérézolFrédérique MagdinierLaurent TichitAnaïs BaudotPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 8, p e1009263 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Elva María Novoa-Del-Toro Efrén Mezura-Montes Matthieu Vignes Morgane Térézol Frédérique Magdinier Laurent Tichit Anaïs Baudot A multi-objective genetic algorithm to find active modules in multiplex biological networks.
description	The identification of subnetworks of interest-or active modules-by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular conditions. We here propose MOGAMUN, a Multi-Objective Genetic Algorithm to identify active modules in MUltiplex biological Networks. MOGAMUN optimizes both the density of interactions and the scores of the nodes (e.g., their differential expression). We compare MOGAMUN with state-of-the-art methods, representative of different algorithms dedicated to the identification of active modules in single networks. MOGAMUN identifies dense and high-scoring modules that are also easier to interpret. In addition, to our knowledge, MOGAMUN is the first method able to use multiplex networks. Multiplex networks are composed of different layers of physical and functional relationships between genes and proteins. Each layer is associated to its own meaning, topology, and biases; the multiplex framework allows exploiting this diversity of biological networks. We applied MOGAMUN to identify cellular processes perturbed in Facio-Scapulo-Humeral muscular Dystrophy, by integrating RNA-seq expression data with a multiplex biological network. We identified different active modules of interest, thereby providing new angles for investigating the pathomechanisms of this disease. Availability: MOGAMUN is available at https://github.com/elvanov/MOGAMUN and as a Bioconductor package at https://bioconductor.org/packages/release/bioc/html/MOGAMUN.html. Contact: anais.baudot@univ-amu.fr.
format	article
author	Elva María Novoa-Del-Toro Efrén Mezura-Montes Matthieu Vignes Morgane Térézol Frédérique Magdinier Laurent Tichit Anaïs Baudot
author_facet	Elva María Novoa-Del-Toro Efrén Mezura-Montes Matthieu Vignes Morgane Térézol Frédérique Magdinier Laurent Tichit Anaïs Baudot
author_sort	Elva María Novoa-Del-Toro
title	A multi-objective genetic algorithm to find active modules in multiplex biological networks.
title_short	A multi-objective genetic algorithm to find active modules in multiplex biological networks.
title_full	A multi-objective genetic algorithm to find active modules in multiplex biological networks.
title_fullStr	A multi-objective genetic algorithm to find active modules in multiplex biological networks.
title_full_unstemmed	A multi-objective genetic algorithm to find active modules in multiplex biological networks.
title_sort	multi-objective genetic algorithm to find active modules in multiplex biological networks.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/c6d3a18612264ec793ee179ae807abcd
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A multi-objective genetic algorithm to find active modules in multiplex biological networks.

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