Table S1 for details. This fact is confirmed by the pairwise comparisons between all classifiers, see Figure \[fig:auc\_boxplot\]B. A green square means that the combination in the row won more frequently over the combination in the column across the data set pairs. The good performance of the single gene classifier is reflected by the fact that the bottom row does not contain a single red box. Also, the generally poor performance of *Taylor* is clearly reflected in the dark red rows associated with this approach.

We also provide the classification results for the LOG classifiers in Figure S1 and Table S2. In general, the performances are lower than for the NMC, with the best combination not even reaching an AUC of 0.7 while several NMC classifiers clearly exceed 0.7. Apart from [*Lee*]{}all composite LOG classifiers perform equally or even worse than the single gene LOG classifier. However, it should be noted that the performance of the LOG classifier is highly variable as a function of the number of included features—see Figures S2-S4. In addition, the training procedure does not converge for all feature values as is evident in the AUC vs. number of features curves that end abruptly. The high 