癌症的发展和进展是由于驱动基因的突变累积所致。正确识别导致癌症发展的驱动基因可以显著地辅助药物设计、癌症诊断和治疗。大多数计算机方法通过假设驱动基因往往一起工作、形成蛋白质复合物和富集通路来检测癌症驱动因子。然而，它们忽略了微核糖核酸(miRNA)调节其靶基因的表达并与人类疾病有关。在这项工作中，我们提出了一种称为GM-GCN的图卷积网络(GCN)方法，用于基于基因-miRNA网络识别癌症驱动基因。首先，我们构建了一个基因-miRNA网络，其中节点是miRNAs和它们的目标基因。连接miRNA和基因的边界表示miRNA和基因之间的调节关系。我们根据它们的生物学特性为miRNA和基因准备了初始特征，并使用GCN模型通过聚合邻近miRNA节点的特征来学习网络中的基因特征表示。然后，学习到的特征通过1D卷积模块进行特征维度变换。我们利用学习到的和原始的基因特征来优化模型参数。最后，从网络中学习的基因特征和初始输入的基因特征被馈送到逻辑回归模型中以预测基因是否为驱动基因。我们应用我们的模型和最先进的方法来预测泛癌症和个体癌症类型的癌症驱动因子。实验结果表明，与基于基因网络的最先进方法相比，我们的模型在受试者工作特征曲线下面积和精度-召回曲线下面积方面表现良好。GM-GCN可以通过https://github.com/weiba/GM-GCN免费获得。©作者（们）2023年。由牛津大学出版社出版。保留所有权利。有关权限，请发电子邮件至journals.permissions@oup.com。

The development and progression of cancer arise due to the accumulation of mutations in driver genes. Correctly identifying the driver genes that lead to cancer development can significantly assist the drug design, cancer diagnosis and treatment. Most computer methods detect cancer drivers based on gene-gene networks by assuming that driver genes tend to work together, form protein complexes and enrich pathways. However, they ignore that microribonucleic acid (RNAs; miRNAs) regulate the expressions of their targeted genes and are related to human diseases. In this work, we propose a graph convolution network (GCN) approach called GM-GCN to identify the cancer driver genes based on a gene-miRNA network. First, we constructed a gene-miRNA network, where the nodes are miRNAs and their targeted genes. The edges connecting miRNA and genes indicate the regulatory relationship between miRNAs and genes. We prepared initial attributes for miRNA and genes according to their biological properties and used a GCN model to learn the gene feature representations in the network by aggregating the features of their neighboring miRNA nodes. And then, the learned features were passed through a 1D convolution module for feature dimensionality change. We employed the learned and original gene features to optimize model parameters. Finally, the gene features learned from the network and the initial input gene features were fed into a logistic regression model to predict whether a gene is a driver gene. We applied our model and state-of-the-art methods to predict cancer drivers for pan-cancer and individual cancer types. Experimental results show that our model performs well in terms of the area under the receiver operating characteristic curve and the area under the precision-recall curve compared to state-of-the-art methods that work on gene networks. The GM-GCN is freely available via https://github.com/weiba/GM-GCN.© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.