median. **B:** This panel shows the result of pairwise comparisons between all combinations of feature extraction methods and secondary data sources. If, for a given combination of training and test data set, the AUC value of classifier $i$ is higher (lower) than the AUC value of classifier $j$ on the same test data set, it is counted as a win (loss) for classifier $i$. Element $(i,j)$ in the matrix represents the $\log_2$ ratio of wins to losses of method $i$ compared to method $j$. Green indicates an overall win, red an overall loss and white represents draws. The rows and columns are sorted as in Panel A. [**Abbreviations:**]{} SG: Single genes; C: [*Chuang*]{}; L: [*Lee*]{}and T: [*Taylor*]{}. []{data-label="fig:auc_boxplot"}](Plots/Boxplot_permuted_Experiment_01_NMCv1.pdf "fig:"){width="3.5in"}

**B**\
![ For each combination of feature extraction method and secondary data source and each pair of data sets we obtained one AUC value resulting in 30 AUC values per combination. The number of features for each classifier was determined in the cross-validation procedure (CV-optimized). **A:** Each box plot shows the median, the $25\%$ and $75\%$ percentiles and the standard deviation of the 30 AUC values. Outliers are depicted by crosses. The boxes are sorted in descending order according to the 