Deep learning for RNA structure prediction.

Journal: Current opinion in structural biology
Published Date: Feb 10, 2025

Abstract

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.

Authors

  • Jiuming Wang
    Department of Computer Science and Engineering, The Chinese University of Hong Kong, Sha Tin, Hong Kong SAR 999077, China.
  • Yimin Fan
    Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Institute of Children's Reading and Learning, Faculty of Psychology, Beijing Normal University, Room 1415, Houzhu Building, No.19 Xinjiekouwai Street, Haidian, Beijing, China.
  • Liang Hong
    Department of Computer Science and Engineering, The Chinese University of Hong Kong, Sha Tin, Hong Kong SAR 999077, China.
  • Zhihang Hu
  • Yu Li
    Department of Public Health, Shihezi University School of Medicine, 832000, China.

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