Generalizable single-cell perturbation response prediction using energy-guided flow matching

The paper introduces scEGFlow, an energy‑guided flow matching framework that predicts how single cells respond to perturbations by modeling continuous transitions from control to perturbed states. It uses conditional flow matching and condition‑specific energy gradients to dynamically adjust predictions without retraining, achieving superior performance on imaging and transcriptomic benchmarks, even for unseen perturbations or limited data. The method preserves cellular manifold geometry, heterogeneity, and accurately recapitulates gene‑expression regulatory patterns.

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• benchmarks