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Federated learning can outperform local training in survival analysis, with Random Survival Forest emerging as the top model for heterogeneous healthcare data.
Causal gene regulatory network inference methods only dominate when data is pristine; common single-cell data pathologies like dropout and latent confounders selectively negate their advantages.
