AINeutralarXiv – CS AI · 9h ago6/10
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You Only Train Once: Differentiable Subset Selection for Omics Data
Researchers introduce YOTO, an end-to-end machine learning framework that simultaneously selects compact gene subsets and performs prediction tasks in single-cell transcriptomic analysis. The differentiable architecture enforces sparsity and uses multi-task learning to improve biomarker discovery while outperforming existing feature selection methods.