虽然找到关键生物标志物并提高疾病模型的准确性很重要，但了解它们的相互作用关系也同样重要。本研究基于图神经网络的结构提出了一种透明稀疏图路径网络（TSGPN）。该网络模拟体内基因的作用，增加先验知识，并提高模型的准确性。首先，根据蛋白质-蛋白质相互作用网络和竞争内源RNA（ceRNA）网络构建图连接，并基于图注意机制和硬具体估计自发地去除一些噪声或不重要的连接。这实现了代表疾病中其他基因对mRNA影响的ceRNA网络的重建。接下来，将基于基因的解释转变为基于通路数据库的基于通路的解释，并添加隐藏层以实现通路的高维分析。最后，实验结果表明，所提出的TSGPN方法在F1分数和AUC上均优于其他比较方法，更重要的是，它可以有效地展示基因的作用。通过应用于肺癌预后的数据分析，发现了与LUSC预后相关的10条通路，以及与这些通路密切相关的关键生物标志物，如HOXA10、hsa-mir-182、LINC02544。还重建了它们之间的关系，以更好地解释疾病的内部机制。版权所有 © 2024 金、石和周。

While it is important to find the key biomarkers and improve the accuracy of disease models, it is equally important to understand their interaction relationships. In this study, a transparent sparse graph pathway network (TSGPN) is proposed based on the structure of graph neural networks. This network simulates the action of genes in vivo, adds to prior knowledge, and improves the model's accuracy. First, the graph connection was constructed according to protein-protein interaction networks and competing endogenous RNA (ceRNA) networks, from which some noise or unimportant connections were spontaneously removed based on the graph attention mechanism and hard concrete estimation. This realized the reconstruction of the ceRNA network representing the influence of other genes in the disease on mRNA. Next, the gene-based interpretation was transformed into a pathway-based interpretation based on the pathway database, and the hidden layer was added to realize the high-dimensional analysis of the pathway. Finally, the experimental results showed that the proposed TSGPN method is superior to other comparison methods in F1 score and AUC, and more importantly, it can effectively display the role of genes. Through data analysis applied to lung cancer prognosis, ten pathways related to LUSC prognosis were found, as well as the key biomarkers closely related to these pathways, such as HOXA10, hsa-mir-182, and LINC02544. The relationship between them was also reconstructed to better explain the internal mechanism of the disease.Copyright © 2024 Jin, Shi and Zhou.