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Generic GA-PPI-Net: Generic Evolutionary Algorithm to Detect Semantic and Topological Biological Communities

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Date
2020
Notes
Cette conférence s'est tenue en ligne.
Dewey
Programmation, logiciels, organisation des données
Sujet
Community Detection; Biological Networks; PPI Networks; Genetic Algorithm; Heuristic Crossover
DOI
http://dx.doi.org/10.5220/0009779902950306
Conference name
15th International Conference on Software Technologies (ICSOFT 2020)
Conference date
07-2020
Author
van Sinderen, Marten; Fill, Hans-Georg; Maciaszek, Leszek
Publisher
SciTe Press
ISBN
978-989-758-443-5
URI
https://basepub.dauphine.fr/handle/123456789/21334
Collections
  • LAMSADE : Publications
Metadata
Show full item record
Author
Ben M'barek, Marwa
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Borgi, A.
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
295-306
Abstract (EN)
Community detection aims to identify topological structures and discover patterns in complex networks. It presents an important problem of great significance in many fields. In this paper, we are interested in the detection of communities in biological networks. These networks represent protein-protein or gene-gene interactions which corresponds to a set of proteins or genes that collaborate at the same cellular function. The goal is to identify such semantic and/or topological communities from gene annotation sources such as Gene Ontology. We propose a Genetic Algorithm (GA) based technique as a clustering approach to detect communities from biological networks. For this purpose, we introduce four 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, an heuristic crossover to strengthen links in the communities and a specific mutation operator. Experimental results show the ability of our Genetic Algorithm to detect communities of genes that are semantically similar or/and interacting.

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