2010 | OriginalPaper | Buchkapitel
Learning Hierarchical Bayesian Networks for Genome-Wide Association Studies
verfasst von : Raphaël Mourad, Christine Sinoquet, Philippe Leray
Erschienen in: Proceedings of COMPSTAT'2010
Verlag: Physica-Verlag HD
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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 10
5
variables for 2000 individuals.