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 105 variables for 2000 individuals. © Springer-Verlag Berlin Heidelberg 2010.
CITATION STYLE
Mourad, R., Sinoquet, C., & Leray, P. (2010). Learning hierarchical Bayesian networks for genome-wide association studies. In Proceedings of COMPSTAT 2010 - 19th International Conference on Computational Statistics, Keynote, Invited and Contributed Papers (pp. 549–556). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-7908-2604-3_56
Mendeley helps you to discover research relevant for your work.