sources may be at a disadvantage compared to single gene classifiers that select the gene set from all genes on the chip. Third, we investigated the effect of sample size. Finally, we looked into the effect of heterogeneity of the data sets on classifier performances. We find that none of these factors change our general findings.

In addition to all these technical factors, we also investigated whether the biological information captured in the secondary data contributes to the classification performance of the composite feature classifiers. To our astonishment we found that composite classifiers constructed from 25 *randomized* versions of the secondary data sources performed comparably to composite classifiers trained on the original, non-randomized data.

We conclude that further research has to be done on finding effective ways to integrate secondary data sources in predictors of outcome in breast cancer. In order to facilitate this research, and to ensure a standardized and objective way of establishing improvements over state-of-the-art approaches, we make all the code, data sets and results employed in this comparison available for download and use upon request.

  [**Method**]{}                     [**Symbol**]{}    [**Description**]{}                                                                                                                                                             [**Secondary**]{} [**data**]{}   [**Feature**]{}                         [**Feature value**]{}
  ---------------------------------- ----------------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -------------------------------- --------------------------------------- -------------------------------------------------------------------------
  Single genes                       SG                Calculates t-statistic between mRNA 