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UID:893@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20151014T110000
DTEND;TZID=Europe/Paris:20151014T120000
DTSTAMP:20150929T090000Z
URL:https://www.i2m.univ-amu.fr/evenements/detection-de-communautes-dans-l
 es-graphes-multiplexes/
SUMMARY: (...): Détection de communautés dans les graphes multiplexes
DESCRIPTION::  Various biological networks can be constructed\, each featur
 ing gene/protein relationships of different meanings (e.g. protein interac
 tions or gene co-expression). However\, this diversity is classically not 
 considered and the different interaction categories are usually aggregated
  in a single network. The multiplex framework\, where biological relations
 hips are represented by different network layers reflecting the various na
 ture of interactions\, is expected to retain more information.We assessed 
 here aggregation\, consensus and multiplex-modularity approaches to detect
  communities from multiple network sources. By simulating random networks\
 , we demonstrated that the multiplex-modularity method outperforms the agg
 regation and consensus approaches when network layers are incomplete or he
 terogeneous in density. Application to a multiplex biological network cont
 aining 4 layers of physical or functional interactions allowed recovering 
 communities more accurately annotated than their aggregated counterparts. 
 Overall\, taking into account the multiplexity of biological networks lead
 s to better-defined functional modules.| Webpage | Webpage ||  |  |
CATEGORIES:Séminaire,Mathématiques-Évolution-Biologie
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DTSTART:20150329T030000
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