addition we used the PPI network published by Chuang [*et al.*]{}[@Chuang2007] (NetC). \[tab:methods\_overview\]

Results {#results .unnumbered}
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Current composite feature classifiers do not outperform single gene classifiers on six breast cancer data sets {#sec:SGbetter .unnumbered}
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We compared the performance of a nearest-mean classifier (NMC) using single genes with a NMC employing feature extraction methods based on pathway and PPI data. The results are depicted in Figure \[fig:auc\_boxplot\]. For each combination of secondary data source and feature extraction approach, Figure \[fig:auc\_boxplot\]A shows the box plots of the area under the receiver-operator characteristics curve (AUC) values obtained for each pair of data sets – using one data set of the pair as training set and the other data set in the pair as test set. The feature extraction approaches are ranked in descending order based on the median AUC values. The box plots suggest that no composite classifier performs better than the single gene classifier. Indeed, testing whether the mean performance of the single gene classifier is different from the mean performance of any composite classifier reveals that there is no difference (null hypothesis can not be rejected) except for [*Taylor*]{}and [*Chuang*]{}-I2D, where the single gene classifier is clearly superior. See 