College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China.
School of Information Science and Technology, Dalian Maritime University, Dalian, Liaoning, China.
PLoS Comput Biol. 2021 Aug 26;17(8):e1009291. doi: 10.1371/journal.pcbi.1009291. eCollection 2021 Aug.
Secondary structure plays an important role in determining the function of noncoding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine learning (ML) technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on ML technologies and a tabularized summary of the most important methods in this field. The current pending challenges in the field of RNA secondary structure prediction and future trends are also discussed.
二级结构在决定非编码 RNA 的功能方面起着重要作用。因此，鉴定 RNA 的二级结构具有重要意义。计算预测是预测 RNA 二级结构的主流方法。不幸的是，尽管在过去的 40 年中提出了新的方法，但计算预测方法的性能在过去十年中已经停滞不前。最近，随着 RNA 结构数据的可用性不断增加，基于机器学习 (ML) 技术的新方法，特别是深度学习，已经缓解了这个问题。在这篇综述中，我们提供了一个基于 ML 技术的 RNA 二级结构预测方法的全面概述，并以表格形式总结了该领域最重要的方法。还讨论了 RNA 二级结构预测领域当前存在的挑战和未来的趋势。
