Probability Flow Matching learns biophysically consistent stochastic processes for gene regulation from time-resolved single-cell measurements, where only the biophysical versions accurately capture lineage transitions, fate specification, and perturbation responses despite similar data fit.
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Learning biophysical models of gene regulation with probability flow matching
Probability Flow Matching learns biophysically consistent stochastic processes for gene regulation from time-resolved single-cell measurements, where only the biophysical versions accurately capture lineage transitions, fate specification, and perturbation responses despite similar data fit.