Intervention-aware distillation transfers mechanistic knowledge from perturbational transcriptomics to imaging phenomics for improved one-shot transfer to unseen drugs and target gene discovery.
Cellflux: Simulating cel- lular morphology changes via flow matching
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ContextFlow regularizes flow matching with a transition plausibility matrix built from local tissue organization and ligand-receptor patterns to produce statistically and biologically coherent trajectories from longitudinal spatial omics data.
CellFluxRL post-trains the CellFlux model with RL using seven biological reward functions to generate more biologically valid virtual cell images.
SFM improves generalization under distribution shift for scientific imaging tasks while AVUQ supplies sample-efficient epistemic and aleatoric uncertainty estimates plus anomaly scores.
citing papers explorer
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Intervention-Aware Multiscale Representation Learning from Imaging Phenomics and Perturbation Transcriptomics
Intervention-aware distillation transfers mechanistic knowledge from perturbational transcriptomics to imaging phenomics for improved one-shot transfer to unseen drugs and target gene discovery.
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ContextFlow: Context-Aware Flow Matching For Trajectory Inference From Spatial Omics Data
ContextFlow regularizes flow matching with a transition plausibility matrix built from local tissue organization and ligand-receptor patterns to produce statistically and biologically coherent trajectories from longitudinal spatial omics data.
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CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning
CellFluxRL post-trains the CellFlux model with RL using seven biological reward functions to generate more biologically valid virtual cell images.
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Uncertainty-Aware Distribution-to-Distribution Flow Matching for Scientific Imaging
SFM improves generalization under distribution shift for scientific imaging tasks while AVUQ supplies sample-efficient epistemic and aleatoric uncertainty estimates plus anomaly scores.