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Scalable Single-Cell Gene Expression Generation with Latent Diffusion Models
arXiv:2511.02986v2 Announce Type: replace-cross Abstract: Computational modeling of single-cell gene expression is crucial for understanding cellular processes, but generating realistic expression profiles remains a major challenge. This difficulty arises from the count nature of gene expression data and complex latent dependencies among genes. Existing generative models often impose artificial gene orderings or rely on shallow neural network architectures.