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
Trajecto- rynet: A dynamic optimal transport network for modeling cellular dynamics
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UNVERDICTED 3representative citing papers
scShapeBench supplies synthetic and real annotated single-cell datasets across four shape categories, with scReebTower outperforming PAGA and Mapper on topology-aware metrics.
MUST-FM is a simulation-free multiscale supervised framework that scales unbalanced optimal transport flow matching for trajectory inference in single-cell data by exploiting hierarchical structure and transition priors.
citing papers explorer
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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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scShapeBench: Discovering geometry from high dimensional scRNAseq data
scShapeBench supplies synthetic and real annotated single-cell datasets across four shape categories, with scReebTower outperforming PAGA and Mapper on topology-aware metrics.
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Multiscale Supervised Unbalanced Optimal Transport Flow Matching
MUST-FM is a simulation-free multiscale supervised framework that scales unbalanced optimal transport flow matching for trajectory inference in single-cell data by exploiting hierarchical structure and transition priors.