sensitivity to the number of features is most evident for the [*Taylor*]{}composite NMCs. Clearly, the LOG classifier as employed here (and as employed by Chuang [*et al.*]{}) requires additional regularization to ensure convergence across the whole range of feature values. Also in combination with this classifier, [*Taylor*]{}performed poorly. This together with the high computational burden associated with this method, prompted us to omit [*Taylor*]{}from the remaining analyses.

Based on the results presented in Figure \[fig:auc\_boxplot\], we conclude that on the six breast cancer data sets employed in this comparison, composite classifiers employing secondary data sources do not outperform single gene classifiers on the task of predicting outcome in breast cancer, provided that a robust single gene classifier is employed.

**A**\
![ 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 