PerturbedVAE disentangles perturbation-specific signals from invariant gene expression structure to recover causal representations and improve out-of-distribution prediction in single-cell perturbation modeling.
Nature Machine Intelligence , volume=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 3years
2026 3roles
background 1polarities
unclear 1representative citing papers
Two new methods distill implicit regulatory knowledge from single-cell foundation models to enable generalizable gene regulatory network inference on unseen data.
citing papers explorer
-
What Makes a Representation Good for Single-Cell Perturbation Prediction?
PerturbedVAE disentangles perturbation-specific signals from invariant gene expression structure to recover causal representations and improve out-of-distribution prediction in single-cell perturbation modeling.
-
Towards Universal Gene Regulatory Network Inference: Unlocking Generalizable Regulatory Knowledge in Single-cell Foundation Models
Two new methods distill implicit regulatory knowledge from single-cell foundation models to enable generalizable gene regulatory network inference on unseen data.
- Skipping the Zeros in Diffusion Models for Sparse Data Generation