AI Insight
BRIDGE is a new computational framework that uses graph neural networks to infer gene regulatory networks from single-cell RNA sequencing data. The method addresses key limitations of existing approaches by incorporating biologically-informed graph refinement based on co-expression patterns and using heterogeneous dynamic gating to control information flow between genes and cells. Testing across multiple benchmark datasets showed BRIDGE achieved state-of-the-art performance, with a 5% improvement in average AUPRC over the next-best method on cell-type-specific networks, and successfully validated 46 of its top 100 novel predictions in human embryonic stem cells using existing ChIP-seq databases.
Why it matters
Understanding gene regulatory networks is fundamental to deciphering how cells control gene expression in different states and conditions. This improved computational method could accelerate discovery of regulatory mechanisms in development and disease without requiring expensive experimental validation for every prediction, and may enable better transfer of regulatory knowledge across different cell types with limited training data.
Understand the Science
arXiv:2606.14734v1 Announce Type: new
Abstract: Motivation: Gene regulatory network inference from single-cell RNA sequencing (scRNA-seq) data is important for uncovering cell-state-specific transcriptional programs. However, scRNA-seq measurements are sparse and noisy, and experimentally validated TF-target interactions remain limited, making reliable inference challenging. Although graph neural networks have advanced GRN prediction, existing methods often rely on biologically unconstrained graph augmentation, such as random edge perturbation, and insufficiently control information transfer between genes and cells. These limitations may distort regulatory structures and weaken robustness under noisy and weakly supervised settings. Results: To address these issues, we propose an innovative framework named Biological Evidence Refinement and Heterogeneous Dynamic Gating for Gene Regulatory Networks (BRIDGE). BRIDGE extracts gene and cell representations from the expression matrix and its matrix dual, and performs contrastive learning in the gene space and cell space between self and neighbors across the co-expression-refined regulatory view and the original graph. It then applies heterogeneous gated encoding to adaptively regulate information transfer between genes and cells, enabling robust transcription factor-to-target gene prediction. Experiments on benchmark datasets spanning three network types and seven cell types show that BRIDGE achieves state-of-the-art AUROC and AUPRC in most settings. In particular, on Specific networks, BRIDGE improves average AUPRC by 5% over the second-best baseline, GCLink. In cross-cell-type few-shot transfer, BRIDGE consistently outperforms GCLink and GENELink across all six target cell types. A case study on hESC further supports the biological relevance of the predictions, with 9 of the top 10 and 46 of the top 100 novel TF-target interactions validated by ChIPBase.
Source: BRIDGE: Biological Evidence Refinement and Heterogeneous Dynamic Gating for Gene Regulatory Networks