biostudies-literatureUnknown12(3)Weng GGene regulatory network (GRN) inference, a process of reconstructing gene regulatory rules from experimental data, has the potential to discover new regulatory rules. However, existing methods often struggle to generalize across diverse cell types and account for unseen regulators. Here, this work presents GRNPT, a novel Transformer-based framework that integrates large language model (LLM) embeddings from publicly accessible biological data and a temporal convolutional network (TCN) autoencoder to capture regulatory patterns from single-cell RNA sequencing (scRNA-seq) trajectories. GRNPT significantly outperforms both supervised and unsupervised methods in inferring GRNs, particularly when training data is limited. Notably, GRNPT exhibits exceptional generalizability, accurately predictinAdvanced science (Weinheim, Baden-Wurttemberg, Germany)e2409990https://www.ebi.ac.uk/biostudies/studies/S-EPMC11744656biostudies-literatureIntegrating Prior Knowledge Using Transformer for Gene Regulatory Network Inference.PMC11744656Kim HWeng GMartin PWon KJfalseIntegrating Prior Knowledge Using Transformer for Gene Regulatory Network Inference.Gene regulatory network (GRN) inference, a process of reconstructing gene regulatory rules from experimental data, has the potential to discover new regulatory rules. However, existing methods often struggle to generalize across diverse cell types and account for unseen regulators. Here, this work presents GRNPT, a novel Transformer-based framework that integrates large language model (LLM) embeddings from publicly accessible biological data and a temporal convolutional network (TCN) autoencoder to capture regulatory patterns from single-cell RNA sequencing (scRNA-seq) trajectories. GRNPT significantly outperforms both supervised and unsupervised methods in inferring GRNs, particularly when training data is limited. Notably, GRNPT exhibits exceptional generalizability, accurately predictin2025-01-01T00:00:00Z2025 Jan2025-04-03T23:58:13.508Z2025-04-03T23:58:13.508ZS-EPMC117446563960518110.1002/advs.202409990