al.*]{}[@Taylor2009]. We investigated how these methods perform with respect to classification accuracy and stability of the set of features included in the classifiers. We will now briefly outline how the approaches work and point out some of the claims made by the authors. Detailed descriptions are provided in the Methods section.

Chuang [*et al.*]{}[@Chuang2007] describe a greedy search algorithm on PPI networks. For each defined subnetwork, a composite feature is defined as a variant of the average of the expression values of the genes included in the subnetwork. The score that guides the search is the association of the composite feature with patient outcome. Significance testing and a feature selection step are employed to select the set of composite features employed in the final classifier. The authors claim that classification based on subnetwork markers improves prediction performance on two breast cancer data sets. Moreover, they state that subnetwork markers are more reproducible across different breast cancer studies than single gene markers.

Lee [*et al.*]{}[@Lee2008] employ gene sets from the MsigDB database [@Subramanian2005] as secondary data source. The association of the composite feature with patient outcome is used as performance criterion, and a greedy search is employed to select a subset 