Yu Haopeng, Qi Yiman, Ding Yiliang
Department of Cell and Developmental Biology, John Innes Centre, Norwich Research Park, Norwich, United Kingdom.
Front Mol Biosci. 2022 May 23;9:869601. doi: 10.3389/fmolb.2022.869601. eCollection 2022.
Deep learning, or artificial neural networks, is a type of machine learning algorithm that can decipher underlying relationships from large volumes of data and has been successfully applied to solve structural biology questions, such as RNA structure. RNA can fold into complex RNA structures by forming hydrogen bonds, thereby playing an essential role in biological processes. While experimental effort has enabled resolving RNA structure at the genome-wide scale, deep learning has been more recently introduced for studying RNA structure and its functionality. Here, we discuss successful applications of deep learning to solve RNA problems, including predictions of RNA structures, non-canonical G-quadruplex, RNA-protein interactions and RNA switches. Following these cases, we give a general guide to deep learning for solving RNA structure problems.
深度学习，即人工神经网络，是一种机器学习算法，它可以从大量数据中解读潜在关系，并已成功应用于解决结构生物学问题，如RNA结构。RNA可以通过形成氢键折叠成复杂的RNA结构，从而在生物过程中发挥重要作用。虽然实验工作已经能够在全基因组范围内解析RNA结构，但深度学习最近才被引入用于研究RNA结构及其功能。在这里，我们讨论深度学习在解决RNA问题上的成功应用，包括RNA结构预测、非经典G-四链体、RNA-蛋白质相互作用和RNA开关。在这些案例之后，我们给出了使用深度学习解决RNA结构问题的一般指南。
