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Fig. 3 | BMC Bioinformatics

Fig. 3

From: A method combining a random forest-based technique with the modeling of linkage disequilibrium through latent variables, to run multilocus genome-wide association studies

Fig. 3

The embedded structure of the T-Trees model. The focus is set on the expansion of one meta-tree. a First meta-node N1. b Extra-tree embedded in meta-node N1. c Details of the Extra-tree embedded in meta-node N1. The value indicated in each leaf is the probability to be a case in this leaf. The five values 0.0008, 0.040, 0.351, 0.635 and 0.999 define the value domain of the meta-variable that corresponds to meta-node N1. d Threshold 0.635 is the best threshold among the five values of the meta-variable to discrimate between affected and unaffected subjects. Node N1 is splitted accordingly. As regards the left subtree expansion of N1, a novel meta-node N2 is created. Right subtree expansion of N1 ends in a meta-leaf (number of subjects below threshold 2000). e Whole meta-tree grown with its two embedded trees

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