You Only Train Once: Differentiable Subset Selection for Omics Data

ArXi:2512.17678v2 Announce Type: replace-cross Selecting compact and informative gene subsets from single-cell transcriptomic data is essential for biomarker discovery, improving interpretability, and cost-effective profiling. However, most existing feature selection approaches either operate as multi-stage pipelines or rely on post hoc feature attribution, making selection and prediction weakly coupled.