用图神经网络分析基因调控网络，提升预测精度与生物洞察。
Analysis of Gene Regulatory Networks from Gene Expression Using Graph Neural Networks
- 基于表达数据与文献构建的图注意力网络预测调控关系。
- 通过注意力机制精准识别关键调控基因，提升网络解析能力。
- 适合生物信息学与精准医学研究者，推动药物发现进展。
解析基因调控网络（GRNs）的复杂性对于理解细胞过程和疾病机制至关重要。传统计算方法难以应对这些网络的动态特性。本研究探索了图神经网络（GNNs）在建模类似GRNs的图结构数据中的应用。采用图注意力网络v2（GATv2），提出一种新型GRN构建与分析方法，结合基因表达数据及文献推导的布尔模型。该模型凭借先进的注意力机制，准确预测调控相互作用并精确定位关键调控因子。结果表明，GNNs有望突破传统局限，提供更丰富的生物学见解。模型的成功依赖于高质量数据，凸显提升数据采集的重要性。GNN在GRN研究中的整合将推动个性化医疗、药物研发及对生物系统认知的发展，依托网络结构分析实现节点与边的更优预测。
原文摘要 · Abstract (English)
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.
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