values for [*Chuang*]{}-KEGG, [*Taylor*]{}-KEGG or [*Taylor*]{}-HPRD since for some data sets, the number of significant composite features was lower than 50. Abbreviations of methods as in Figure \[fig:auc\_boxplot\]. []{data-label="fig:boxplot50"}](Plots/Boxplot_Experiment_01_50_features_NMCv1.pdf){width="5in"}

### Restricted gene sets are not detrimental to composite feature classifiers {#restricted-gene-sets-are-not-detrimental-to-composite-feature-classifiers .unnumbered}

We next hypothesized that the lack of difference in the performance between composite classifiers and single gene classifiers could be caused by the fact that the composite features are bound to the genes annotated in the secondary data while single gene classifiers can employ all genes on the microarray. To test this hypothesis we retrained the single gene classifier, but restricted the set of genes from which features for the final classifier could be selected to the genes that are present in the respective secondary data sources. The resulting classifiers are denoted by the secondary data source from which the gene set is derived, while the single gene classifier employing features from the whole microarray is denoted by *unrestr*. The results of this analysis are depicted in Figure \[fig:restricted\_genes\]A. There is significant difference in the performance of the classifiers employing genes annotated in the I2D, KEGG and MsigDB (Table S9). However, when accounting for multiple testing only the 