PACE recovers geometry-consistent continuous transport dynamics from single-cell time-course snapshots via state-time dependent anisotropic Riemannian metrics, alternating cross-time couplings, and neural bridges, outperforming baselines by 23.7% on average in reconstruction metrics across seven to九
Rna velocity of single cells.Nature, 560(7719):494–498, 2018
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CT-OT Flow estimates continuous-time dynamics from discrete temporal snapshots by using partial optimal transport to align intervals and kernel smoothing to reconstruct distributions for ODE/SDE training.
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PACE: Geometry-Aware Bridge Transport for Single-Cell Trajectory Inference
PACE recovers geometry-consistent continuous transport dynamics from single-cell time-course snapshots via state-time dependent anisotropic Riemannian metrics, alternating cross-time couplings, and neural bridges, outperforming baselines by 23.7% on average in reconstruction metrics across seven to九
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CT-OT Flow: Estimating Continuous-Time Dynamics from Discrete Temporal Snapshots
CT-OT Flow estimates continuous-time dynamics from discrete temporal snapshots by using partial optimal transport to align intervals and kernel smoothing to reconstruct distributions for ODE/SDE training.