396-scPEFT: Fine-Tuning for Single-Cell Foundation Models

Paper Talk · Jan 13, 2026 · 13m

Researchers have developed scPEFT, a framework that utilizes parameter-efficient fine-tuning to adapt large-scale single-cell foundation models to diverse biological contexts. Traditional methods of updating these models often require massive computational power and risk catastrophic forgetting, where the system loses its original learned knowledge. By using pluggable adapters like LoRA and prefix tuning, scPEFT significantly reduces the number of trainable parameters while improving performance on tasks such as cell-type identification and batch correction. This approach proves especially…