CoDiffGRN: Rethinking Gene Regulatory Network Inference via the BEELINE-KGC Benchmark and Co-evolutionary Discrete Diffusion

BoYueGRN: Zero-shot causal discovery of directed gene regulatory networks from single-cell transcriptomes via amortized inference over synthetic structural causal models

CoReGRN: a context-aware post-processing framework for gene regulatory network inference revealing regulatory signatures in HIV-Leishmaniasis.

Accurate inference of gene regulatory networks (GRNs) from single-cell gene expression data is challenging due to noise, data sparsity, and variability in gene-gene associations across cells. We propose CoReGRN (Contextual Refinement of Gene Regulatory Networks), a nonparametric, context-aware post-processing framework that refines inferred GRNs by reweighting candidate regulatory interactions using Mutual Information based association strength and local network context. The method uses empirical cumulative distribution function (ECDF) based scores to assess how unusual each interaction is relative to the connectivity patterns of the genes it connects. Then combines this contextual information with the original edge confidence scores. We evaluate the CoReGRN framework on gold-standard datasets from the BEELINE benchmark suite across multiple state-of-the-art GRN inference algorithms. The results show consistent performance improvements, with average absolute gains of 0.097 in AUROC, 0.130 in AUPR, 0.133 in MCC, and 0.095 in F1-Score. We further apply the framework to a single-cell HIV-Leishmaniasis dataset, where the refined networks support the analysis of disease-specific regulatory hubs and interactions. Comparison with existing biological knowledge identifies both known and potentially novel regulatory relationships across HIV infection, HIV-Leishmaniasis, and HIV-Visceral Leishmaniasis conditions. The analysis includes hub gene identification, interaction network analysis, and literature based validation using GeneMANIA.

MultiCausGRN: directed prior-guided graph attention model for multi-omics gene regulatory network inference

Direct prior-guided graph learning can improve the robustness and biological interpretability of GRN inference in data-limited settings and demonstrate that directed prior-guided graph learning can improve the robustness and biological interpretability of GRN inference in data-limited settings.

scReGAT: Leveraging Knowledge of Regulatory Interactions to Predict Long-range Gene Regulation at Single-cell Resolution.

Understanding gene regulation at single-cell resolution is crucial for unraveling development, disease, and cellular identity. We introduce single-cell regulatory graph attention network (scReGAT), a deep learning framework that integrates prior knowledge of cis-regulatory element (cRE)-gene and transcription factor-gene interactions to reconstruct cell-specific regulatory networks. Central to scReGAT is a knowledge-guided regulatory graph (kRG), which combines experimentally validated regulatory interactions with cell-resolved chromatin accessibility profiles. These graphs serve as the foundation for training a Graph Attention Network (GAT) to predict gene expression and quantify the contribution of specific regulatory interactions using an interpretable regulatory score for each edge. In benchmarking across five single-cell multi-omics datasets, scReGAT successfully recapitulates known cell-type-specific cRE-gene interactions. In both neuroblastoma and osteogenic differentiation systems, it uncovers dynamic regulatory rewiring that predicts transcriptional transitions. Furthermore, by integrating genome-wide association studies loci from Alzheimer's disease, multiple sclerosis, and schizophrenia, scReGAT identifies disease-associated cell types and uncovers candidate regulatory mechanisms underlying complex trait associations. These results position scReGAT as a robust and generalizable framework for decoding long-range gene regulation at single-cell resolution. The source code of scReGAT can be accessed at https://github.com/TianLab-Bioinfo/scReGAT/ and https://ngdc.cncb.ac.cn/biocode/tool/BT008081.

IAGRN: An Interleaved-Attention Graph Neural Network for Gene Regulatory Network Inference

A structure-aware interleaved-attention graph learning framework, termed IAGRN, is proposed for GRN inference from scRNA-seq data that interleaves topology-constrained local attention with distance-aware global attention, enabling effective integration of structural priors and long-range regulatory signals.

NSSGRN: Network Structure Selection Method for Gene Regulatory Network Construction.

Gene regulatory networks (GRNs) play essential roles in cellular control and various biological processes. Analyzing gene expression data and inferring GRNs provides crucial insights into organismal growth, development, and disease mechanisms. However, prevailing inference approaches often concentrate on a limited gene expression feature set and tend to analyze network structure from a single perspective, thus restricting a comprehensive understanding of gene relationships. To address this issue, we introduce a novel network structure selection method for GRN construction (NSSGRN), considers the isomorphism and complementarity of network structures generated by several classical methods, and integrates them to infer the network structure. Specifically, NSSGRN firstly generates an initial gene relationship prioritization from knockout data. Second, several methods are integrated by considering the isomorphism and complementarity of their results. Finally, the integrated network structure is optimized by a scoring based method to increase true positives and reduce false positives. Experiments on two challenging datasets (25 networks in total) shows that NSSGRN outperforms nine other advanced methods in overall performance, demonstrating its effectiveness in enhancing the accuracy of GRN construction. The code is available at https://github.com/Xtu-LWGroup/NSSGRN.git.