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Fig. 2 | BMC Bioinformatics

Fig. 2

From: Machine learning approach informs biology of cancer drug response

Fig. 2

Pathways that inform ML210 response. A Visualization of gene distribution within modules. Relevant genes are those that passed the Boruta filtering step. X-axis denote total number genes per module, y-axis denotes number of relevant genes, and the shading indicates total number of modules. B Ten-fold  cross validation error as a function of number of features used in the SVM model. Dashed line indicates no information rate, i.e. the error made if the class with the greatest frequency was selected. C Minimum feature ranking for each module. D GO Biological Processes pathway enrichment of genes contained within modules presented in C). P-values shown are corrected for multiple hypothesis testing using the Holm-Bonferroni method

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