2024/03/04 by Akhila Krishna, Ravi Kant Gupta, Krishna, Akhila +5
Biochemistry, Genetics and Molecular Biology · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Biological sciences #FOS: Computer and information sciences #Gene expression and cancer classification #Genetics, Bioinformatics, and Biomedical Research #Genomics (q-bio.GN) #Quantitative Methods (q-bio.QM) #Tissues and Organs (q-bio.TO)
paper · pdf · doi:10.48550/arxiv.2403.01927
openalex publication_date 2024/03/04 · openalex created_date 2024/03/06 · openalex updated_date 2026/07/28
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.