scYeast: a biological-knowledge-guided foundation model on yeast single-cell transcriptomics

Source: PubMed Central Open Access, NCBI / U.S. National Library of Medicine

Though large-scale pre-trained models are vital for foundational cell modeling, most of them focus on human or mouse systems, with less emphasis on model organisms like yeast (), and fail to use existing biological prior knowledge effectively. Here, we present scYeast, the first foundational cell model for yeast single-cell transcriptomics that effectively embeds biological priors. scYeast employs a novel asymmetric parallel architecture to infuse transcriptional regulatory information into the Transformer's attention mechanism, leveraging biological knowledge during training. Pre-trained on large-scale yeast single-cell transcriptomics data, scYeast demonstrates strong generalization and biological interpretability. It shows capability in zero-shot tasks, such as inferring regulatory relationships. After fine-tuning, scYeast performs well in diverse tasks, including cell state classification, growth doubling time prediction, and gene perturbation response prediction. Additionally, using transfer learning, scYeast can be adapted to other omics datasets, such as proteomics, thus broadening its utility. Overall, scYeast is a promising tool for yeast single-cell biology research and presents a new framework for integrating foundational models with biological priors, accelerating discovery in yeast synthetic and systems biology and providing a replicable framework for other organisms. abs0010