  Gene selection plays a pivotal role in oncology research for improving
outcome prediction accuracy and facilitating cost-effective genomic profiling
for cancer patients. This paper introduces two gene selection strategies for
deep learning-based survival prediction models. The first strategy uses a
sparsity-inducing method while the second one uses importance based gene
selection for identifying relevant genes. Our overall approach leverages the
power of deep learning to model complex biological data structures, while
sparsity-inducing methods ensure the selection process focuses on the most
informative genes, minimizing noise and redundancy. Through comprehensive
experimentation on diverse genomic and survival datasets, we demonstrate that
our strategy not only identifies gene signatures with high predictive power for
survival outcomes but can also streamlines the process for low-cost genomic
profiling. The implications of this research are profound as it offers a
scalable and effective tool for advancing personalized medicine and targeted
cancer therapies. By pushing the boundaries of gene selection methodologies,
our work contributes significantly to the ongoing efforts in cancer genomics,
promising improved diagnostic and prognostic capabilities in clinical settings.
