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dc.contributor.authorMoretti, Stefano
dc.contributor.authorFragnelli, Vito
dc.contributor.authorPatrone, Fioravante
dc.contributor.authorBonassi, Stefano
dc.date.accessioned2010-09-13T07:53:45Z
dc.date.available2010-09-13T07:53:45Z
dc.date.issued2010
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/4731
dc.language.isoenen
dc.subjectcoalitional gamesen
dc.subjectbiological networksen
dc.subjectgene interactionen
dc.subject.ddc003en
dc.titleUsing coalitional games on biological networks to measure centrality and power of genesen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenMOTIVATION: The interpretation of gene interaction in biological networks generates the need for a meaningful ranking of network elements. Classical centrality analysis ranks network elements according to their importance but may fail to reflect the power of each gene in interaction with the others. RESULTS: We introduce a new approach using coalitional games to evaluate the centrality of genes in networks keeping into account genes' interactions. The Shapley value for coalitional games is used to express the power of each gene in interaction with the others and to stress the centrality of certain hub genes in the regulation of biological pathways of interest. The main improvement of this contribution, with respect to previous applications of game theory to gene expression analysis, consists in a finer resolution of the gene interaction investigated in the model, which is based on pair-wise relationships of genes in the network. In addition, the new approach allows for the integration of a priori knowledge about genes playing a key function on a certain biological process. An approximation method for practical computation on large biological networks, together with a comparison with other centrality measures, is also presented.en
dc.relation.isversionofjnlnameBioinformatics
dc.relation.isversionofjnlvol26
dc.relation.isversionofjnlissue21
dc.relation.isversionofjnldate2010
dc.relation.isversionofjnlpages2721-2730
dc.relation.isversionofdoihttp://dx.doi.org/10.1093/bioinformatics/btq508en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherOxford University Pressen
dc.subject.ddclabelRecherche opérationnelleen


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