Learning Genomic Representations to Predict Clinical Outcomes in Cancer
This work addresses the challenge of predicting clinical outcomes for cancer patients using genomic data, which is incremental as it applies a neural network approach to an existing problem.
The paper tackled predicting patient survival in cancer from genomic data, demonstrating that neural network-based genomic representations outperform existing survival analysis methods on brain tumor data.
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.