molecular mechanisms governing survival in breast cancer.

The underlying idea of these methods is that genes do not act in isolation, and that complex diseases such as cancer are actually caused by the deregulation of complete processes or pathways, representing ‘hallmarks of cancer’ [@Hanahan2011]. This is unlikely to happen due to an aberration in a single gene, and often multiple genes need to be perturbed to disable a process. This leads to the notion that aggregating gene expression of functionally linked genes smooths out noise and provides more power to detect deregulation of complete functional units and hence to obtain a clearer picture of the biological process underlying tumorigenesis and disease outcome.

The observed improvement in classification accuracy achieved by the approaches employing secondary data is hard to assess since it is dependent on many factors such as the specific data sets and evaluation protocol employed. To shed more light on this issue we performed an extensive comparison of a simple, single gene based classifier with three of the most popular approaches that include secondary data sources in the construction of the classifier. More specifically, we included the approaches proposed by Chuang [*et al.*]{}[@Chuang2007], Lee [*et al.*]{}[@Lee2008] and Taylor [*et 