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Genetic Algorithm for Community Detection in Biological Networks

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Date
2018
Dewey
Programmation, logiciels, organisation des données
Sujet
community detection; biological networks; Gene Ontology; Genetic Algorithm; Kyoto Encyclopedia of Genes; Genomes (KEGG) database
Journal issue
Procedia Computer Science
Volume
126
Publication date
2018
Article pages
195-204
Publisher
Elsevier
DOI
http://dx.doi.org/10.1016/j.procs.2018.07.233
URI
https://basepub.dauphine.fr/handle/123456789/19252
Collections
  • LAMSADE : Publications
Metadata
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Author
Ben M'barek, Marwa
253759 Laboratoire d'Informatique, Programmation, Algorithmique et Heuristique [LIPAH]
Borgi, Amel
253759 Laboratoire d'Informatique, Programmation, Algorithmique et Heuristique [LIPAH]
Bedhiafi, Walid
status unknown
Ben Hmida, Sana
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Type
Article accepté pour publication ou publié
Abstract (EN)
We are interested in the detection of communities in biological networks. We focus more precisely on gene interaction networks. They represent protein-protein or gene-gene interactions. A community in such networks corresponds to a set of proteins or genes that collaborate at the same cellular function. Our goal is to identify such network or community from gene annotation sources such as Gene Ontology (GO). In this paper, we propose a Genetic Algorithm (GA) based approach to discover communities in a gene interaction network. Special solution coding and mutation operator are introduced. Otherwise, we propose a specific fitness function based on similarity measure and interaction value between genes. Experiments on real data extracted from the well-known Kyoto Encyclopedia of Genes and Genomes (KEGG) database show the ability of the proposed method to successfully detect existing or even new communities.

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