  Genomics are rapidly transforming medical practice and basic biomedical
research, providing insights into disease mechanisms and improving therapeutic
strategies, particularly in cancer. The ability to predict the future course of
a patient's disease from high-dimensional genomic profiling will be essential
in realizing the promise of genomic medicine, but presents significant
challenges for state-of-the-art survival analysis methods. In this abstract we
present an investigation in learning genomic representations with neural
networks to predict patient survival in cancer. We demonstrate the advantages
of this approach over existing survival analysis methods using brain tumor
data.
