College of Electronics and Information Engineering Guangdong Ocean University, Zhanjiang, China and the Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
Brief Bioinform. 2022 Nov 19;23(6). doi: 10.1093/bib/bbac364.
Emerging evidence indicates that circular RNAs (circRNAs) can provide new insights and potential therapeutic targets for disease diagnosis and treatment. However, traditional biological experiments are expensive and time-consuming. Recently, deep learning with a more powerful ability for representation learning enables it to be a promising technology for predicting disease-associated circRNAs. In this review, we mainly introduce the most popular databases related to circRNA, and summarize three types of deep learning-based circRNA-disease associations prediction methods: feature-generation-based, type-discrimination and hybrid-based methods. We further evaluate seven representative models on benchmark with ground truth for both balance and imbalance classification tasks. In addition, we discuss the advantages and limitations of each type of method and highlight suggested applications for future research.
新兴证据表明，环状 RNA（circRNA）可以为疾病的诊断和治疗提供新的见解和潜在的治疗靶点。然而，传统的生物学实验既昂贵又耗时。最近，具有更强表示学习能力的深度学习使其成为预测疾病相关 circRNA 的一种很有前途的技术。在这篇综述中，我们主要介绍了与 circRNA 相关的最流行的数据库，并总结了基于深度学习的三种 circRNA-疾病关联预测方法：基于特征生成、基于类型区分和基于混合的方法。我们进一步在具有真实数据的基准上评估了七种有代表性的模型，包括平衡和不平衡分类任务。此外，我们还讨论了每种方法的优缺点，并强调了对未来研究的建议应用。
