Finetuning masking challenges narrow-task evaluation of cell foundation models (opens in new tab)

Single-cell foundation models are large, self-supervised deep learning networks pretrained on millions of cellular transcriptomes. These models promise to deliver cell representations that are transferable across diverse biological domains and, when used in specific tasks, would outperform narrowly scoped models. A central assumption is that more pretraining data translates to better downstream performance. However, despite its centrality, this assumption remains largely untested. Here, we te...