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Zhao, Q., Chen, J., Zhao, Z., Mao, Q., Shi, H., & Fan, X. ML-Based RNA Secondary Structure Prediction Methods: A Survey. AI Medicine. 2024. doi: https://doi.org/10.53941/aim.2024.100006
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Article

ML-Based RNA Secondary Structure Prediction Methods: A Survey

Qi Zhao 1, Jingjing Chen 1, Zheng Zhao 2, Qian Mao 3, Haoxuan Shi 1 and Xiaoya Fan 4,∗

1 School of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110000, China

2 School of Artificial Intelligence, Dalian Maritime University, Dalian 116000, China

3 Department of Food Science and Engineering, College of Light Industry, Liaoning University, Shenyang 110000, China

4 School of Software, Dalian University of Technology, Key Laboratory for Ubiquitous Network and Service Software, Dalian 116000, China

∗ Correspondence: xiaoyafan@dlut.edu.cn

Received: 6 May 2024; Revised: 17 October 2024; Accepted: 22 October 2024; Published: 29 October 2024

 

Abstract: The secondary structure of noncoding RNAs (ncRNA) is significantly related to their functions, emphasizing the importance and value of identifying ncRNA secondary structure. Computational prediction methods have been widely used in this field. However, the performance of existing computational methods has plateaued in recent years despite various advancements. Fortunately, the emergence of machine learning, particularly deep learning, has brought new hope to this field. In this review, we present a comprehensive overview of machine learning-based methods for predicting RNA secondary structures, with a particular emphasis on deep learning approaches. Additionally, we discuss the current challenges and prospects in RNA secondary structure prediction.

Keywords:

RNA secondary structure prediction machine learning; deep learning

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