环状RNA（circRNAs）是一类具有闭合环状结构的非编码RNA分子。已经证实它们在许多疾病的减轻中发挥了重要作用。此外，许多疾病的临床诊断和治疗研究已经揭示circRNA可以被认为是一种潜在的生物标志物。因此，了解circRNA与疾病的关联可以帮助预测一些生活活动的障碍。然而，传统的生物实验方法耗时较长。基于机器学习的circRNA-疾病关联预测最常见的方法可以避免这个问题，它依赖于多样化的数据。然而，这些方法通常不涉及circRNA和疾病的拓扑信息。此外，circRNA可以通过miRNA与疾病相关联。鉴于这些考虑，我们提出了一种名为THGNCDA的新方法来预测circRNA和疾病之间的关联。具体而言，对于一对特定的circRNA和疾病，我们使用带有注意力机制的图神经网络来学习其每个邻居的重要性。此外，我们使用多层卷积神经网络来根据它们的属性探索circRNA-疾病对之间的关系。计算嵌入时，我们引入miRNA的信息。实验结果表明THGNCDA优于SOTA方法。此外，我们可以观察到我们的方法具有更好的召回率。为了确认注意力的重要性，我们进行了大量的削弱实验。对尿路膀胱肿瘤和前列腺肿瘤的案例研究进一步显示了THGNCDA在发现circRNA候选子与疾病之间已知关系方面的能力。© 2023作者。版权所有。请电邮: journals.permissions@oup.com 申请权限。

Circular RNAs (circRNAs) are a class of noncoding RNA molecules featuring a closed circular structure. They have been proved to play a significant role in the reduction of many diseases. Besides, many researches in clinical diagnosis and treatment of disease have revealed that circRNA can be considered as a potential biomarker. Therefore, understanding the association of circRNA and diseases can help to forecast some disorders of life activities. However, traditional biological experimental methods are time-consuming. The most common method for circRNA-disease association prediction on the basis of machine learning can avoid this, which relies on diverse data. Nevertheless, topological information of circRNA and disease usually is not involved in these methods. Moreover, circRNAs can be associated with diseases through miRNAs. With these considerations, we proposed a novel method, named THGNCDA, to predict the association between circRNAs and diseases. Specifically, for a certain pair of circRNA and disease, we employ a graph neural network with attention to learn the importance of its each neighbor. In addition, we use a multilayer convolutional neural network to explore the relationship of a circRNA-disease pair based on their attributes. When calculating embeddings, we introduce the information of miRNAs. The results of experiments show that THGNCDA outperformed the SOTA methods. In addition, it can be observed that our method gives a better recall rate. To confirm the significance of attention, we conducted extensive ablation studies. Case studies on Urinary Bladder and Prostatic Neoplasms further show THGNCDA's ability in discovering known relationships between circRNA candidates and diseases.© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.