investigate these hypotheses one by one.

### The number of selected features does not effect relative performances {#the-number-of-selected-features-does-not-effect-relative-performances .unnumbered}

In the cross-validation protocol that we proposed for unbiased performance evaluation and also employed in the comparison, we employ individual feature filtering to select an optimized number of features to employ in the classifier. While this approach is sub-optimal, we (Wessels [*et al.*]{}[@Wessels2005]) and others have shown that these simple approaches perform the best in predicting phenotypes based on gene expression data. However, we observed in the curves showing the AUC values as a function of the number of ranked features included in the classifier (Figures S2-S4) that the AUC values for the NMC are very stable across a large range of features for most approaches, and that the absolute maximal AUC value chosen during the feature selection routine might only marginally differ from the performance obtained with other feature values. For this reason, and since the selection of the optimized number of features introduces additional variability between the approaches, we decided to fix the number of features to 50, 100 and 150 for most approaches. We chose these values as they covered the feature ranges across which the performance remained 