Wang Jiuming, Fan Yimin, Hong Liang, Hu Zhihang, Li Yu
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China.
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China.
Curr Opin Struct Biol. 2025 Apr;91:102991. doi: 10.1016/j.sbi.2025.102991. Epub 2025 Feb 10.
Predicting RNA structures from sequences with computational approaches is of vital importance in RNA biology considering the high costs of experimental determination. AI methods have revolutionized this field in recent years, enabling RNA structure prediction with increasingly higher accuracy and efficiency. With an increase in the number of models proposed for this task, this review presents a timely summary of the applications of AI, particularly deep learning, in RNA structure prediction, highlighting their methodology advances as well as the challenges and opportunities for further work in this field.
鉴于实验测定成本高昂,利用计算方法从序列预测RNA结构在RNA生物学中至关重要。近年来,人工智能方法彻底改变了这一领域,使RNA结构预测的准确性和效率越来越高。随着针对该任务提出的模型数量不断增加,本综述及时总结了人工智能,特别是深度学习在RNA结构预测中的应用,突出了它们在方法上的进展以及该领域进一步研究的挑战和机遇。