GRNPred: A Multimodal Graph Transformer with Masked Gene Expression Pretraining for Gene Regulatory Network Inference
Gene regulatory network (GRN) inference is a fundamental problem in systems biology, aiming to identify transcription factor (TF)-target gene interactions from high-dimensional gene expression data. Accurate GRN reconstruction remains challenging due to limited labeled regulatory data, severe class imbalance, and the complex, nonlinear nature of transcriptional regulation. Here, we introduce GRNPred, a multimodal graph transformer framework for robust GRN inference that integrates gene expression, functional annotations, semantic gene descriptions, regulatory binding motif priors, and gene co-expression network topology. GRNPred follows a two-stage training strategy. In the first self-supervised pretraining phase, a graph transformer encoder is trained on TF-centered gene co-expression subgraphs using masked gene-expression reconstruction, enabling the model to learn transcriptional context from unlabeled data. In the second supervised fine-tuning stage, the pretrained encoder is finetuned for supervised TF-target edge prediction using available regulatory annotations. Transformer-based attention allows GRNPred to capture long-range and context-dependent regulatory interactions that are difficult to model with conventional graph neural networks. Extensive evaluation across 7 benchmark datasets and 3 regulatory network constructions demonstrates that GRNPred consistently outperforms state-of-the-art GRN inference methods, achieving AUROC scores of up to 0.94 and AUPRC scores of up to 0.93, while maintaining strong robustness across diverse biological contexts.