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Apprentissage de réseaux bayésiens hiérarchiques latents pour les études d'association pangénomiques

Abstract : We propose a new hierarchical latent class model devoted to represent statistical dependencies between genetic markers, in the human genome. Our proposal relies on a forest of hierarchical latent class models. The motivation is the reduction of dimension of the data to be further submitted to statistical association tests with respect to diseased/non diseased status. An 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.
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https://hal.archives-ouvertes.fr/hal-00484706
Contributor : Christine Sinoquet <>
Submitted on : Tuesday, May 18, 2010 - 8:03:23 PM
Last modification on : Thursday, February 7, 2019 - 2:23:08 PM
Long-term archiving on: : Thursday, September 16, 2010 - 2:40:00 PM

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Raphaël Mourad, Christine Sinoquet, Philippe Leray. Apprentissage de réseaux bayésiens hiérarchiques latents pour les études d'association pangénomiques. Proc. JFRB 2010, 5th French-speaking meeting on Bayesian networks, Nantes, May 2010, Nantes, France. pp.11-12. ⟨hal-00484706⟩

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