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A central problem in cancer genomics is to identify interpretable biomarkers for better disease prognosis. Many of the biomarkers identified through Cox Proportional Hazard (PH) models are biologically uninterpretable. We propose the use of graph Laplacian regularized Cox PH model to integrate biological networks into the feature selection problem in survival analysis. Simulation studies demonstrate that the performance of the proposed algorithm is superior to L1 and L1+L2 regularized Cox PH models. Utility of this algorithm is also validated by its ability to identify key known biomarkers such as p53 and myc in estrogen receptor positive breast cancer patients using genomic abberration data generated by the Cancer Genome Altas consortium. With the rapid expansion of our knowledge of biological networks, this approach will become increasingly useful for mining high-throughput genomic datasets.