Genetic Algorithm for Community Detection in Biological Networks

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.
Document type :
Journal articles
Complete list of metadatas

Cited literature [31 references]  Display  Hide  Download

https://hal-univ-paris10.archives-ouvertes.fr/hal-02286078
Contributor : Sana Ben Hamida <>
Submitted on : Friday, September 13, 2019 - 2:28:30 PM
Last modification on : Tuesday, October 22, 2019 - 1:32:12 AM

File

1-s2.0-S1877050918312092-main ...
Publisher files allowed on an open archive

Identifiers

Citation

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⟩

Share

Metrics

Record views

8

Files downloads

12