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Réseaux bayésiens hiérarchiques avec variables latentes pour la modélisation des dépendances entre SNP: une approche pour les études d'association pangénomiques

Abstract : Discover the genetic basis of common genetic diseases represents a public health issue. However this task presents several difficulties such as high data dimensionality and identification of the causal mutations. For this purpose, the modelling of dependencies between genetic markers using hierarchical bayesian networks offers several possibilities: data dimension reduction through latent variables and identification of causal markers through the conditional independence property.
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https://hal.archives-ouvertes.fr/hal-00484705
Contributor : Christine Sinoquet <>
Submitted on : Tuesday, May 18, 2010 - 7:46:41 PM
Last modification on : Thursday, February 7, 2019 - 2:23:39 PM
Long-term archiving on: : Thursday, September 16, 2010 - 2:38:49 PM

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Raphaël Mourad, Christine Sinoquet, Philippe Leray. Réseaux bayésiens hiérarchiques avec variables latentes pour la modélisation des dépendances entre SNP: une approche pour les études d'association pangénomiques. Proc. SFC 2010, XVIIth Join Meeting of the French Society of Classification, France, Saint-Denis de la Réunion, 9-11 june, Jun 2010, Saint-Denis de la Réunion, France. pp.25-29. ⟨hal-00484705⟩

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