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Learning Hierarchical Bayesian Networks for Genome-Wide Association Studies

Abstract : We describe a novel probabilistic graphical model customized to represent the statistical dependencies between genetic markers, in the Human genome. Our proposal relies on a forest of hierarchical latent class models. The 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 has been shown to be tractable on benchmarks describing 100 000 variables for 2000 individuals.
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Contributor : Christine Sinoquet <>
Submitted on : Tuesday, May 18, 2010 - 7:12:57 PM
Last modification on : Thursday, January 17, 2019 - 10:40:04 AM


  • HAL Id : hal-00484696, version 1



Raphaël Mourad, Christine Sinoquet, Philippe Leray. Learning Hierarchical Bayesian Networks for Genome-Wide Association Studies. COMPSTAT, Nineteenth International Conference on Computational Statististics, Aug 2010, Paris, France. pp.549-556. ⟨hal-00484696⟩



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