RT Journal Article
SR Electronic
T1 A Multi-Objective Genetic Algorithm to Find Active Modules in Multiplex Biological Networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.05.25.114215
DO 10.1101/2020.05.25.114215
A1 Elva-María Novoa-del-Toro
A1 Efrén Mezura-Montes
A1 Matthieu Vignes
A1 Frédérique Magdinier
A1 Laurent Tichit
A1 Anaïs Baudot
YR 2020
UL http://biorxiv.org/content/early/2020/05/26/2020.05.25.114215.abstract
AB 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.Contact elva.novoa{at}inserm.fr, anais.baudot{at}univ-amu.frCompeting Interest StatementThe authors have declared no competing interest.