Synthetic Single Cell

Synthetic single-cell research aims to create realistic digital representations of cells, primarily to overcome limitations in acquiring and annotating large biological datasets for AI-driven analysis. Current efforts leverage various generative models, including generative adversarial networks (GANs), diffusion models, and flow-based models, often incorporating biophysical principles to enhance realism and accuracy. This work is crucial for advancing biomedical image analysis, accelerating drug discovery, and improving our understanding of fundamental cellular processes through in silico experimentation and data augmentation.