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Fig. 5 | Clinical Epigenetics

Fig. 5

From: Machine-learned analysis of global and glial/opioid intersection–related DNA methylation in patients with persistent pain after breast cancer surgery

Fig. 5

Analysis of the drop in the classification accuracy (Table 3) of five different algorithms (classification and regression trees (CART), k-nearest neighbors (kNN), support vector machines (SVM), multinomial regression (“regression”), and naïve Bayes adaptive classification) when the methylation information, originally comprising a total of d = 14 CpG sites located in OPRM1, TLR4, or LINE1, was reduced to two or one genes. The numbers indicate the difference in classification accuracy, obtained in several training scenarios of reduced sets of gene-specific CpG islands, to that obtained with the respective algorithm when trained with the full data set. Subsequently, applying hierarchical clustering (Ward [67]) to these differences, a pattern of two groups of the tested scenarios emerged. In the first cluster (top), the accuracy did not change when using a reduced data set for training. By contrast, the accuracy dropped in scenarios included in the second cluster (bottom). The figure has been created using the R software package (version 3.4.4; http://CRAN.R-project.org/ [40]) and the “heatmap.2” function of the R package “gplots” (G.R. Warnes; https://cran.r-project.org/package=gplots)

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