Zhou Junliang, Gong Ningji, Hu Yanjun, Yu Hong, Wang Guoyin, Wu Hao
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, No. 2 Chongwen Road, Nan'an District, Chongqing 400065, China.
Department of Emergency, The Second Hospital, Cheeloo College of Medicine, Shandong University, No. 247 Beiyuan Street, Tianqiao District, Jinan 250033, Shandong, China.
Brief Bioinform. 2025 Jul 2;26(4). doi: 10.1093/bib/bbaf349.
The reconstruction of gene regulatory networks (GRNs) is crucial for uncovering regulatory relationships between genes and understanding the mechanisms of gene expression within cells. With advancements in single-cell RNA sequencing (scRNA-seq) technology, researchers have sought to infer GRNs at the single-cell level. However, existing methods primarily construct global models encompassing entire gene networks. While these approaches aim to capture genome-wide interactions, they frequently suffer from decreased accuracy due to challenges such as network scale, noise interference, and data sparsity. This study proposes GRANet (Graph Residual Attention Network), a novel deep learning framework for inferring GRNs. GRANet leverages residual attention mechanisms to adaptively learn complex gene regulatory relationships while integrating multi-dimensional biological features for a more comprehensive inference process. We evaluated GRANet across multiple datasets, benchmarking its performance against state-of-the-art methods. The experimental results demonstrate that GRANet consistently outperforms existing methods in GRN inference tasks. In addition, in our case study on EGR1, CBFB, and ELF1, GRANet achieved high prediction accuracy, effectively identifying both known and novel regulatory interactions. These findings highlight GRANet's potential to advance research in gene regulation and disease mechanisms.
基因调控网络（GRNs）的重建对于揭示基因之间的调控关系以及理解细胞内基因表达机制至关重要。随着单细胞RNA测序（scRNA-seq）技术的进步，研究人员试图在单细胞水平上推断基因调控网络。然而，现有方法主要构建涵盖整个基因网络的全局模型。虽然这些方法旨在捕捉全基因组的相互作用，但由于网络规模、噪声干扰和数据稀疏性等挑战，它们常常准确性降低。本研究提出了GRANet（图残差注意力网络），这是一种用于推断基因调控网络的新型深度学习框架。GRANet利用残差注意力机制自适应地学习复杂的基因调控关系，同时整合多维度生物学特征以进行更全面的推断过程。我们在多个数据集上评估了GRANet，并将其性能与现有最先进方法进行基准测试。实验结果表明，在基因调控网络推断任务中，GRANet始终优于现有方法。此外，在我们对早期生长反应蛋白1（EGR1）、核心结合因子β（CBFB）和E74样因子1（ELF1）的案例研究中，GRANet实现了高预测准确性，有效地识别了已知和新的调控相互作用。这些发现凸显了GRANet在推进基因调控和疾病机制研究方面的潜力。
