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v1 (2010-01-05)

v2 (2010-01-19)

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

Raphaël Mourad 1, Christine Sinoquet (  Corresponding author ) 1, Philippe Leray 1

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

(2010-01-05)

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

Subject

:

Computer Science/Bioinformatics Life Sciences/Quantitative Methods

Keyword(s): bioinformatics – biomedical data analysis – bayesian networks – hierarchical latent class model – data dimensionality reduction – modelling of genetic marker dependencies

hal-00444087, version 1

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

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From: Christine Sinoquet <>

Submitted on: Tuesday, 5 January 2010 16:10:59

Updated on: Tuesday, 19 January 2010 17:58:00