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

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

1 LINA - Laboratoire d'Informatique de Nantes Atlantique

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.

Mots clés: Bayesian networks; hierarchical latent class model; data dimensionality reduction; genetic marker dependency modelling

Domaines: Bio-informatique [q-bio.QM]; Bio-Informatique, Biologie Systémique [q-bio.QM]

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⟩