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[ **A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer**]{}

**[C Staiger$^{1, 2, *}$, S Cadot$^2$, R Kooter$^{3}$, M Dittrich$^{4}$, T Müller$^{4}$, GW Klau$^{1, 5, +, *}$, LFA Wessels$^{2, 3, 6, +, *}$ ]{}**

$^1$ Centrum Wiskunde & Informatica, Life Sciences Group, Science Park 123, 1098 XG Amsterdam, Netherlands\
$^2$ Bioinformatics and Statistics, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, Netherlands\
$^3$ Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, 2600 GA Delft, Netherlands\
$^4$ Department of Bioinformatics, Biocenter, Am Hubland, 97074 University of Würzburg, Germany\
$^5$ Netherlands Institute for Systems Biology, Amsterdam, Netherlands\
$^6$ Cancer Systems Biology Center, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, Netherlands\
$^\ast$ E-mail: c.staiger@cwi.nl, gunnar.klau@cwi.nl, l.wessels@nki.nl\
$^+$ shared last authorship

Abstract {#abstract .unnumbered}
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Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that 