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Learning a forest of Hierarchical Bayesian Networks to model dependencies between genetic markers

Raphaël Mourad 1, Christine Sinoquet (  Auteur correspondant ) 1, Philippe Leray 1

BIL Project (Bioinformatics Research Project of the Pays de la Loire Region) Collaboration(s)

(05/01/2010)

We propose a novel probabilistic graphical model dedicated to represent the statistical dependencies between genetic markers, in the Human genome. Our proposal relies on building a forest of hierarchical latent class models. It is able to account for both local and higher-order dependencies between markers. Our motivation is to reduce the dimension of the data to be further submitted to statistical association tests with respect to diseased/non diseased status. A generic algorithm, CFHLC, has been designed to tackle the learning of both forest structure and probability distributions. A first implementation of CFHLC has been shown to be tractable on benchmarks describing 100000 variables for 2000 individuals, on a standard personal computer.

1 :	Laboratoire d'Informatique de Nantes Atlantique (LINA)
	CNRS : UMR6241 – Université de Nantes – École Nationale Supérieure des Mines - Nantes

Domaine

:

Informatique/Bio-informatique Sciences du Vivant/Bio-Informatique, Biologie Systémique

Mots Clés : bioinformatics – biomedical data analysis – bayesian networks – hierarchical latent class model – data dimensionality reduction – modelling of genetic marker dependencies

hal-00444087, version 2

http://hal.archives-ouvertes.fr/hal-00444087

oai:hal.archives-ouvertes.fr:hal-00444087

Contributeur : Christine Sinoquet <>

Soumis le : Mardi 19 Janvier 2010, 18:00:38

Dernière modification le : Mardi 19 Janvier 2010, 20:49:12