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0Single-cell foundation models are transforming computational biology by leveraging massive multi-omic and spatial atlases to learn transferable representations of cellular states. These frameworks utilize diverse tokenization strategies and neural network backbones, such as transformers and large language models, to process sparse and high-dimensional biological data. Despite their growing scale, current architectures face significant open challenges regarding zero-shot generalization, perturbation modeling fidelity, and evaluation standards. Nevertheless, these models are increasingly shifting from descriptive feature extractors to experiment-guiding systems capable of generating testable biological hypotheses and simulating cellular dynamics.
References:
Jiang S, Fang Z, Zhang Y, et al. The Landscape of Single-Cell Foundation Models: Design Principles, Applications, and Open Challenges[J]. 2026.
