Advancing Gene Selection in Oncology: A Fusion of Deep Learning and Sparsity for Precision Gene Selection

arXiv:2403.01927v1 Announce Type: cross
Abstract: 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 …

abstract accuracy arxiv cancer cost cs.cv deep learning fusion gene genomic oncology paper patients pivotal precision prediction prediction models profiling q-bio.gn q-bio.qm q-bio.to research role sparsity strategies strategy survival type