School of Computer Science and Engineering, Dalian Minzu University, 116600, Dalian, China.
SEAC Key Laboratory of Big Data Applied Technology, Dalian Minzu University, 116600, Dalian, China.
Brief Bioinform. 2022 Sep 20;23(5). doi: 10.1093/bib/bbac379.
CircularRNA (circRNA) is a class of noncoding RNA with high conservation and stability, which is considered as an important disease biomarker and drug target. Accumulating pieces of evidence have indicated that circRNA plays a crucial role in the pathogenesis and progression of many complex diseases. As the biological experiments are time-consuming and labor-intensive, developing an accurate computational prediction method has become indispensable to identify disease-related circRNAs.
We presented a hybrid graph representation learning framework, named GraphCDA, for predicting the potential circRNA-disease associations. Firstly, the circRNA-circRNA similarity network and disease-disease similarity network were constructed to characterize the relationships of circRNAs and diseases, respectively. Secondly, a hybrid graph embedding model combining Graph Convolutional Networks and Graph Attention Networks was introduced to learn the feature representations of circRNAs and diseases simultaneously. Finally, the learned representations were concatenated and employed to build the prediction model for identifying the circRNA-disease associations. A series of experimental results demonstrated that GraphCDA outperformed other state-of-the-art methods on several public databases. Moreover, GraphCDA could achieve good performance when only using a small number of known circRNA-disease associations as the training set. Besides, case studies conducted on several human diseases further confirmed the prediction capability of GraphCDA for predicting potential disease-related circRNAs. In conclusion, extensive experimental results indicated that GraphCDA could serve as a reliable tool for exploring the regulatory role of circRNAs in complex diseases.
环状 RNA（circRNA）是一类具有高度保守性和稳定性的非编码 RNA，被认为是重要的疾病生物标志物和药物靶点。越来越多的证据表明，circRNA 在许多复杂疾病的发病机制和进展中起着至关重要的作用。由于生物学实验既耗时又耗力，因此开发一种准确的计算预测方法来识别与疾病相关的 circRNA 已变得不可或缺。
我们提出了一种混合图表示学习框架，称为 GraphCDA，用于预测潜在的 circRNA-疾病关联。首先，构建了 circRNA-circRNA 相似性网络和疾病-疾病相似性网络，分别用于描述 circRNA 和疾病之间的关系。其次，引入了一种结合图卷积网络和图注意力网络的混合图嵌入模型，用于同时学习 circRNA 和疾病的特征表示。最后，将学习到的表示拼接起来，构建用于识别 circRNA-疾病关联的预测模型。一系列实验结果表明，GraphCDA 在几个公共数据库上优于其他最先进的方法。此外，当仅使用少量已知的 circRNA-疾病关联作为训练集时，GraphCDA 也能取得良好的性能。此外，对几种人类疾病进行的案例研究进一步证实了 GraphCDA 预测潜在疾病相关 circRNA 的能力。总之，大量的实验结果表明，GraphCDA 可以作为探索 circRNA 在复杂疾病中调控作用的可靠工具。
