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Genetic Algorithm to Detect Different Sizes' Communities from Protein Genetic Algorithm to Detect Different Sizes’ Communities from Protein-Protein Interaction Networks 1,3 a 1,2 3 b 3 Marwa Ben M’Barek , Amel Borgi , Sana Ben Hmida and Marta Rukoz 1LIPAH, Faculté des Sciences de Tunis, Université de Tunis El Manar2092, Tunis, Tunisia 2Institut Supérieur d'Informatique, Université de Tunis El Manar, 1002, Tunis, Tunisia 3LAMSADE CNRS UMR 7243, Paris Dauphine University, PSL Research University, Place du Maréchal de Lattre de Tassigny, Paris, France Keywords: Community Detection, Genetic Algorithm, Semantic Similarity, Protein-Protein or Gene-Gene Interaction Networks, Gene Ontology. Abstract: The community detection in large networks is an important problem in many scientific fields ranging from Biology to Sociology and Computer Science. In this paper, we are interested in the detection of communities in the Protein-protein or Gene-gene Interaction (PPI) 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 communities from gene annotation sources such as Gene Ontology. We propose a Genetic Algorithm based approach to detect communities having different sizes from PPI networks. For this purpose, we use a fitness function based on a similarity measure and the interaction value between proteins or genes. Moreover, a specific solution for representing a community and a specific mutation operator are introduced. In the computational tests carried out in this work, the introduced algorithm achieved excellent results to detect existing or even new communities from Protein-protein or Gene-gene Interaction networks. 1 INTRODUCTION genes and the edges correspond to pairwise interactions between genes or proteins. These Community detection in networks is one of the most communities give us an idea about the perception of popular topics of modern network science (Fortunato the network’s structure. The ultimate goal in biology and Hric, 2016). It deals with an interesting is to determine how genes or proteins encode function computational technique for the analysis of networks. in the cell. This work is multidisciplinary as it brings It can yield useful insights into the structural the field of biology and computer science in the broad organization of a network and can serve as a basis for sense. understanding the correspondence between structure Thus, the goal is to find communities having a and function (specific to the domain of the network). biological sense (that participate in the same Identifying the community structure allows us to biological processes or that perform together specific obtain some important information about the biological functions) from gene annotation sources. relationship and interaction among nodes. To make this task, we have combined three levels of In this paper, we are interested in detecting information: communities in biological networks. These networks 1. Semantic level: information contained in biological have received much attention in the past few years ontologies such as Gene Ontology GO (Ashburner et since they model the complex interactions occurring al., 2000) and information obtained by the use of a among different components in the cell (Pizzuti and similarity measure such as GS2 (Ruths et al., 2009), Rombo, 2014). We mainly focus on Protein-protein it assess the semantic similarity between proteins or or Gene-gene interaction networks known as PPI genes. networks. Their nodes correspond to proteins or a https://orcid.org/0000-0002-8307-3533 b https://orcid.org/0000-0003-4202-613X 359 Ben M’Barek, M., Borgi, A., Ben Hmida, S. and Rukoz, M. Genetic Algorithm to Detect Different Sizes’ Communities from Protein-Protein Interaction Networks. DOI: 10.5220/0007836703590370 In Proceedings of the 14th International Conference on Software Technologies (ICSOFT 2019), pages 359-370 ISBN: 978-989-758-379-7 Copyright c 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved ICSOFT 2019 - 14th International Conference on Software Technologies 2. Functional level: information contained in public a specific mutation operator adapted to our problem. databases describing the interactions of proteins or The algorithm outputs the final community structure genes such as Search Tool for Recurring Instances of by selectively exploring the search space. Neighbouring Gene (STRING) database (Mering et Experiments on real datasets show the ability of the al., 2003). proposed approach to correctly detect communities 3. Networks level: information contained in pathway having different sizes. databases that present community of proteins or genes The contents are organized in six main sections. such as KEGG database (Kanehisa and Goto, 2000). The next section provides the necessary background In (Guo et al., 2006), the performed tests revealed of literature survey to community’s detection that genes or proteins in the same community of the methods. Section 3 describes the biological field and biological pathway database KEGG are semantically the data used to formalize the problem. Section 4 similar and are interacting. From this affirmation, we depicts our main proposed algorithm for community have proposed to take into account the similarity detection. In section 5, experimental results on real between proteins or genes that are annotated by terms data sets are presented and analyzed. Finally, section of Gene Ontology (GO). In a previous work, we have 6 draws the conclusion. tested different similarity measures to determine the most suitable one for this problem that is GS2 (Ben M'barek et al., 2018). 2 COMMUNITY DETECTION A lot of research effort has been put into community detection in different academic fields METHODS such as physics, mathematics and computer science. Meanwhile, various algorithms based on Genetic This section presents a brief overview of the existing Algorithms (GA) have been proposed. These community detection algorithms. In recent years, algorithms are used to overcome some drawbacks many authors have given contributions to detect such as scaling up of network size. Indeed some of communities in large networks. The literature survey the community detection algorithms are unsuitable is divided into two categories: community detection for very large networks and require a priori on the basis of analytical approaches and those based knowledge about the community structure, as the on evolutionary approaches (Pizzuti, 2018) . number and the size of communities which is not easy Analytical community methods firstly split or impossible to obtain in real-world networks networks into subgroups according to their (Tasgin et al., 2007). The algorithms based on GA are topological characteristics, then the modularity very effective for community detection especially in assessment is carried out. The most famous and very large complex networks (Jia et al., 2012). prominent algorithms in the literature have been However, the vast majority of optimization methods presented by Girvan and Newman (Girvan and proposed to detect community in PPI networks only Newman, 2002; Newman and Girvan, 2004). Girvan use graph topology and do not use similarity and Newman have proposed the modularity measures between proteins or genes (Pizzuti and assessment criterion, which is one of the most Rombo, 2014). important assessment criteria in the community This paper presents a new community detection detection problem. According to the Girvan and algorithm in PPI networks based on Genetic Newman algorithm, the betweenness score (the Algorithm (GA). This work is a generalization of a number of shortest paths between all vertex pairs that previous method that extract communities of a fixed run along the edge) is computed for all edges in the size (Ben M’barek et al., 2018). Therefore, we network in the first stage. In the second stage, the propose a GA based approach that allows to find edge that has the maximum betweenness score is communities having different sizes. Alike the removed, and betweenness score is computed again previous algorithm, the proposed method uses the for the all affected edges. In the final stage, the similarity measures between proteins or genes and previous two stages are repeated until there are no tries to find the best community structure by more edges in the network. maximizing the concept of community measure. It is Analytical algorithms do not reach the expected different from the community score introduced by successful results in community detection from Pizzuti which is not related to the density of groups complex networks. Therefore, various optimization (Pizzuti, 2008). Our measure is based on semantic based algorithms have been proposed to provide similarity and interaction between proteins or genes. different approaches to solve the community Moreover, we propose a new genetic operation that is detection problem (Atay et al., 2017). With the 360 Genetic Algorithm to Detect Different Sizes’ Communities from Protein-Protein Interaction Networks modularity criterion, the community detection . A biological pathway is a series of actions problem has become an optimization problem. Since among molecules in a cell that leads to a certain 2002, several methods that divide networks product or a change in the cell (National into clusters according to the modularity criterion Human Genome Research Institute (NHGRI), have been developed (Atay et al., 2017) . 2015). There are many types of biological
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