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About this paper
Intekhab Hossain, Jonas Fischer, Rebekka Burkholz, John Quackenbush
DASH uses known gene-interaction information alongside learned weights to guide neural-network pruning, aiming to retain regulatory structure while simplifying gene-expression models. On simulated SIM350 data with 5% noise, DASH reaches 90.7% balanced accuracy at 94.6% sparsity, outperforming SparseFlow in edge recovery. Real-data evaluations use reference networks rather than complete ground truth, so they do not establish causal recovery.
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