Unraveling the complexities of Gene Regulatory Networks (GRNs) is crucial for understanding cellular processes and disease mechanisms. Traditional computational methods often struggle with the dynamic nature of these networks. This study explores the use of Graph Neural Networks (GNNs), a powerful approach for modeling graph-structured data like GRNs. Utilizing a Graph Attention Network v2 (GATv2), our study presents a novel approach to the construction and interrogation of GRNs, informed by gene expression data and Boolean models derived from literature. The model's adeptness in accurately predicting regulatory interactions and pinpointing key regulators is attributed to advanced attention mechanisms, a hallmark of the GNN framework. These insights suggest that GNNs are primed to revolutionize GRN analysis, addressing traditional limitations and offering richer biological insights. The success of GNNs, as highlighted by our model's reliance on high-quality data, calls for enhanced data collection methods to sustain progress. The integration of GNNs in GRN research is set to pioneer developments in personalized medicine, drug discovery, and our grasp of biological systems, bolstered by the structural analysis of networks for improved node and edge prediction.

翻译：揭示基因调控网络的复杂性对于理解细胞过程与疾病机制至关重要。传统的计算方法往往难以应对这些网络的动态特性。本研究探索了使用图神经网络这一强大方法来建模如基因调控网络这类图结构数据。通过采用Graph Attention Network v2，我们提出了一种基于基因表达数据及文献衍生的布尔模型来构建和解析基因调控网络的新方法。该模型在准确预测调控相互作用及识别关键调控因子方面的卓越能力，归功于GNN框架标志性的先进注意力机制。这些发现表明，图神经网络有望彻底革新基因调控网络分析领域，克服传统方法的局限并提供更丰富的生物学洞见。正如我们的模型对高质量数据的依赖所凸显的，图神经网络的成功要求改进数据收集方法以持续推动进展。通过结合网络结构分析以提升节点与边预测能力，图神经网络在基因调控网络研究中的整合应用，必将引领个性化医疗、药物发现及生物系统认知领域的突破性发展。