Abstract : 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.
https://hal-univ-paris10.archives-ouvertes.fr/hal-02286078
Contributor : Sana Ben Hamida
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Submitted on : Friday, September 13, 2019 - 2:28:30 PM
Last modification on : Tuesday, October 22, 2019 - 1:32:12 AM
Marwa Ben M’barek, Amel Borgi, Walid Bedhiafi, Sana Ben Hmida. Genetic Algorithm for Community Detection in Biological Networks. Procedia Computer Science, Elsevier, 2018, 126 (6), pp.195-204. ⟨10.1016/j.procs.2018.07.233⟩. ⟨hal-02286078⟩
