A Generalizable Transformer Framework for Gene Regulatory Network Inference from Single‐Cell Transcriptomes

Gene regulatory network (GRN) inference has advanced substantially through supervised and unsupervised learning approaches; however, many existing methods require extensive computational resources and exhibit limited generalizability across biological contexts. In addition, they do not fully exploit the growing availability of large‐scale transcriptomic and regulatory data. Here, we present FTGRN (Foundation Transformer for Gene Regulatory Networks), a universal framework for GRN inference based on a pretrain–finetune paradigm. FTGRN integrates gene embeddings derived from Generative Pre‐trained Transformer‐4 (GPT‐4) with publicly available chromatin immunoprecipitation sequencing (ChIP‐seq) data to construct a regulatory knowledge base for pretraining a Transformer‐based graph neural network. The pretrained model is subsequently fine‐tuned using single‐cell RNA sequencing (scRNA‐seq) data to infer context‐specific regulatory networks. Leveraging its pretrained foundation, FTGRN enables near real‐time GRN inference, generating networks for 2,000 genes in under 30 seconds, substantially outperforming state‐of‐the‐art methods in both speed and predictive accuracy. Application to amino acid–starved mouse embryonic fibroblasts demonstrated that FTGRN accurately reconstructs stress‐response GRNs and identifies key regulators, including C/EBPγ, c‐Jun, DDIT4, and c‐Fos. Collectively, FTGRN provides a scalable, adaptable, and interpretable framework for GRN inference in single‐cell genomics.