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

Marwa Ben M’Barek, Amel Borgi, Sana Ben Hmida, Marta Rukoz


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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