School of Mathematics and Physics, China University of Geosciences, 388 Lumo Road, Hongshan District, 430074, Wuhan, Hubei, China.
Brief Funct Genomics. 2024 Jul 19;23(4):384-394. doi: 10.1093/bfgp/elad042.
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.
环状 RNA（circRNAs）是一类具有闭合环状结构的非编码 RNA 分子。它们已被证明在许多疾病的减轻中发挥着重要作用。此外，许多关于疾病的临床诊断和治疗的研究表明，circRNA 可以被视为一种潜在的生物标志物。因此，了解 circRNA 与疾病的关联有助于预测一些生命活动的障碍。然而，传统的生物学实验方法耗时耗力。基于机器学习的 circRNA-疾病关联预测最常用的方法可以避免这种情况，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。考虑到这些因素，我们提出了一种新的方法，称为 THGNCDA，用于预测 circRNA 和疾病之间的关联。具体来说，对于特定的 circRNA 和疾病对，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探索基于 circRNA-疾病对属性的关系。在计算嵌入时，我们引入了 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，我们进行了广泛的消融研究。对膀胱癌和前列腺癌的案例研究进一步表明了 THGNCDA 发现 circRNA 候选物与疾病之间已知关系的能力。
