期刊
FRONTIERS IN MOLECULAR BIOSCIENCES
卷 9, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fmolb.2022.869601
关键词
deep learning; RNA secondary structure; RNA tertiary structure; RNA structure prediction; RNA G-quadruplex; RNA-protein interaction
The article discusses the successful applications of deep learning in solving RNA structure problems, including RNA structure prediction, non-canonical G-quadruplex, RNA-protein interactions, and RNA switches. It also provides a general guide to using deep learning for solving RNA structure problems.
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.
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