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. We introduce a scalable latent diffusion model for single-cell gene expression data, which we refer to as scLDM, that respects the fundame
The proliferation of advanced diffusion models for image and text generation is now being applied to complex biological data, addressing long-standing challenges in computational biology.
Improved and scalable modeling of single-cell gene expression will accelerate drug discovery, disease understanding, and the development of new biological interventions.
The ability to generate realistic and scalable single-cell gene expression profiles changes how researchers can computationally explore cellular processes and develop new biological hypotheses.
- · Biotech companies
- · Pharmaceutical R&D
- · Computational biologists
- · AI/ML researchers in biology
- · Traditional wet-lab experimental methods (relatively)
Faster and more accurate in-silico drug target identification.
Reduced costs and timelines for early-stage therapeutic development.
The acceleration of synthetic biology applications through predictive biological design.
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