MultiGRNFormer: A Transformer-Based Multi-Omics GRN Inference Framework

Gene Regulatory Network (GRN) describes the regulatory interactions between transcription factors (TFs) and their target genes, playing a crucial role in understanding developmental biology, disease mechanisms, and drug target discovery. However, due to the complexity of gene regulation, inferring GRNs solely from gene expression data remains highly challenging. Additionally, deep learning models’ dependency high-quality annotation data further constrains their performance improvement. To address these challenges, this study proposes MultiGRNFormer, a Transformer-based model for multi-omics GRN inference. The key innovations of this model include: (1) Integration of transcriptomic and chromatin accessibility data—leveraging a Transformer encoder to effectively capture gene regulatory relationships and improve inference accuracy. (2) Incorporation of a positional encoding mechanism, enabling the model to be sensitive to the order of input features, and meanwhile the use of data augmentation strategies to generate diverse samples, thereby enhancing the utilization of training data. To evaluate the model’s performance, we conducted experiments on seven different single-cell multi-omics datasets and compared it with existing GRN inference methods. The results demonstrate that by combining multi-omics data integration with data augmentation strategies, superior performance in GRN inference tasks can be achieved. Our findings provide new insights for future deep learning-based GRN inference research.