1. A Game Theoretic Neighbourhood-Based Relevance Index
- Author
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Giulia Cesari, Juan A. Nepomuceno, Encarnación Algaba, Stefano Moretti, Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos, Universidad de Sevilla. Departamento de Matemática Aplicada II (ETSI), Dipartimento di Matematica, 'Francesco Brioschi', Politecnico di Milano [Milan] (POLIMI), Departamento de Matemática Aplicada I (IMUS), IMUS, Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE), Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Department of Languages and Computer Systems (LSI-US), and University of Sevilla
- Subjects
0303 health sciences ,Theoretical computer science ,Computer science ,Gene regulatory network ,Complex network ,Complex Networks ,03 medical and health sciences ,0302 clinical medicine ,[INFO]Computer Science [cs] ,Centrality ,Game theory ,Neighbourhood (mathematics) ,030217 neurology & neurosurgery ,Biological network ,Axiom ,Network Analysis ,030304 developmental biology ,Network analysis - Abstract
Studies in Computational Intelligence book series (SCI, volume 689); Centrality measures are used in network analysis to identify the relevant elements in a network. Recently, several centrality measures based on coalitional game theory have been successfully applied to different kinds of biological networks, such as brain networks, gene networks, and metabolic networks. We propose an approach, using coalitional games, to the problem of identifying relevant genes in a biological network. Our model generalizes the notion of degree centrality, whose correlation with the relevance of genes for different biological functions is supported by several practical evidences in the literature. The new relevance index we propose is characterized by a set of axioms defined on gene networks and a formula for its computation is provided. Furthermore, an application to the analysis of a large co-expression network is shortly presented.
- Published
- 2017