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Background: Predictive, stable and interpretable gene signatures aregenerally seen as an important step towards a better personalized medicine.During the last decade various methods have been proposed for that purpose.However, one important obstacle for making gene signatures a standard tool inclinics is the typical low reproducibility of these signatures combined withthe difficulty to achieve a clear biological interpretation. For that purposein the last years there has been a growing interest in approaches that try tointegrate information from molecular interaction networks. Results: We proposea novel algorithm, called FrSVM, which integrates protein-protein interactionnetwork information into gene selection for prognostic biomarker discovery. Ourmethod is a simple filter based approach, which focuses on central genes withlarge differences in their expression. Compared to several other competingmethods our algorithm reveals a significantly better prediction performance andhigher signature stability. More- over, obtained gene lists are highly enrichedwith known disease genes and drug targets. We extendd our approach further byintegrating information on candidate disease genes and targets of diseaseassociated Transcript Factors (TFs).

Integrating Prior Knowledge Into Prognostic Biomarker Discovery based on Network Structure