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GA-PPI-Net: A Genetic Algorithm for Community Detection in Protein-Protein Interaction Networks

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Date
2020
Dewey
Programmation, logiciels, organisation des données
Sujet
Community detection; Genetic algorithm Protein-Protein or gene-gene interaction networks; Semantic Similarity; Gene Ontology
DOI
http://dx.doi.org/10.1007/978-3-030-52991-8_7
Conference name
14th International Conference, ICSOFT 2019 (Revised Selected Papers)
Conference date
07-2020
Conference city
Prague
Conference country
Czech Republic
Book title
Software Technologies
Author
van Sinderen, Marten; Maciaszek, Leszek A.
Publisher
Springer International Publishing
Publisher city
Berlin Heidelberg
Pages number
229
ISBN
978-3-030-52990-1; 978-3-030-52991-8
Book URL
10.1007/978-3-030-52991-8
URI
https://basepub.dauphine.fr/handle/123456789/21129
Collections
  • LAMSADE : Publications
Metadata
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Author
Ben M’barek, Marwa
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Borgi, Amel
253759 Laboratoire d'Informatique, Programmation, Algorithmique et Heuristique [LIPAH]
Ben Hmida, Sana
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Rukoz, Marta
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Type
Communication / Conférence
Item number of pages
133-155
Abstract (EN)
Community detection has become an important research direction for data mining in complex networks. It aims to identify topological structures and discover patterns in complex networks, which presents an important problem of great significance. In this paper, we are interested in the detection of communities in the Protein-Protein or Gene-gene Interaction (PPI) networks. These networks represent a set of proteins or genes that collaborate at the same cellular function. The goal is to identify such semantic and topological communities from gene annotation sources such as Gene Ontology. We propose a Genetic Algorithm (GA) based approach to detect communities having different sizes from PPI networks. For this purpose, we introduce three specific components to the GA: a fitness function based on a similarity measure and the interaction value between proteins or genes, a solution for representing a community with dynamic size and a specific mutation operator. In the computational tests carried out in this work, the introduced algorithm achieved excellent results to detect existing or even new communities from PPI networks.

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