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Figure 13 | BMC Bioinformatics

Figure 13

From: A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-wide association studies

Figure 13

Impact of LD degree on the construction of Forests of Hierarchical Latent Class models. Five sequences showing variable LD degrees have been used to learn Forests of Hierarchical Latent Class Models. For display convention and node nomenclature, see Figure 6. We recall that in any FHLCM graph, edges are directed from top to bottom. a = 0.2, b = 2, card max = 20, t CAST = 0.95, t MI = quantile MI (0.95), t = 0.6 (for CFHLC parameter description, see Section Algorithm).

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BMC Bioinformatics

ISSN: 1471-2105

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