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

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dc.citation.issue8
dc.citation.volume17
dc.contributor.authorNovoa-Del-Toro EM
dc.contributor.authorMezura-Montes E
dc.contributor.authorVignes M
dc.contributor.authorTérézol M
dc.contributor.authorMagdinier F
dc.contributor.authorTichit L
dc.contributor.authorBaudot A
dc.contributor.editorJensen P
dc.coverage.spatialUnited States
dc.date.accessioned2024-01-11T20:16:40Z
dc.date.accessioned2024-07-25T06:32:23Z
dc.date.available2021-08-30
dc.date.available2024-01-11T20:16:40Z
dc.date.available2024-07-25T06:32:23Z
dc.date.issued2021-08-30
dc.description.abstractThe 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.
dc.description.confidentialfalse
dc.format.paginatione1009263-
dc.identifier.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/34460810
dc.identifier.citationNovoa-Del-Toro EM, Mezura-Montes E, Vignes M, Térézol M, Magdinier F, Tichit L, Baudot A. (2021). A multi-objective genetic algorithm to find active modules in multiplex biological networks.. PLoS Comput Biol. 17. 8. (pp. e1009263-).
dc.identifier.doi10.1371/journal.pcbi.1009263
dc.identifier.eissn1553-7358
dc.identifier.elements-typejournal-article
dc.identifier.issn1553-734X
dc.identifier.numbere1009263
dc.identifier.piiPCOMPBIOL-D-20-01734
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/70378
dc.languageeng
dc.publisherPLOS
dc.publisher.urihttps://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009263
dc.relation.isPartOfPLoS Comput Biol
dc.rights(c) The author/sen
dc.rights.licenseCC BY 4.0en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectAlgorithms
dc.subjectComputational Biology
dc.subjectComputer Simulation
dc.subjectDatabases, Nucleic Acid
dc.subjectGene Regulatory Networks
dc.subjectHumans
dc.subjectModels, Biological
dc.subjectModels, Genetic
dc.subjectMuscular Dystrophy, Facioscapulohumeral
dc.subjectRNA-Seq
dc.subjectSoftware
dc.subjectSystems Biology
dc.subjectSystems Integration
dc.subjectSystems Theory
dc.subjectTranscriptome
dc.titleA multi-objective genetic algorithm to find active modules in multiplex biological networks
dc.typeJournal article
pubs.elements-id448333
pubs.organisational-groupOther
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