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ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 12, Issue 9 (September 2025), Pages: 61-78

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 Review Paper

Deep learning and attention mechanisms in RNA secondary structure prediction: A critical survey

 Author(s): 

 Musaab Nabil Ali Askar 1, Azian Azamimi Abdullah 1, 2, *, Mohd Yusoff Mashor 1, Zeti-Azura Mohamed-Hussein 3, Zeehaida Mohamed 4, Wei Chern Ang 5, Shigehiko Kanaya 6

 Affiliation(s):

  1Faculty of Electronic Engineering and Technology, Universiti Malaysia Perlis, Arau, Malaysia
  2Advanced Sensor Technology, Centre of Excellence (CEASTech), Universiti Malaysia Perlis (UniMAP), Arau, Malaysia
  3Department of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia
  4Department of Medical Microbiology and Parasitology, School of Medical Sciences, Universiti Sains Malaysia, George Town, Malaysia
  5Clinical Research Centre, Hospital Tuanku Fauziah, Ministry of Health Malaysia, Perlis, Kangar, Malaysia
  6Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, Japan

 Full text

    Full Text - PDF

 * Corresponding Author. 

   Corresponding author's ORCID profile:  https://orcid.org/0000-0001-5851-7705

 Digital Object Identifier (DOI)

  https://doi.org/10.21833/ijaas.2025.09.006

 Abstract

The secondary structure of ribonucleic acid (RNA) plays a key role in understanding gene regulation, cellular processes, and the development of new treatments. Traditional thermodynamic methods, especially those using Minimum Free Energy (MFE) algorithms, have provided a reliable physics-based approach for predicting RNA structures. Although these methods remain important, there is increasing interest in using deep learning models to detect new structural patterns, such as pseudoknots and long-range interactions, in large RNA datasets. Building on thermodynamic principles, these models aim to extend current knowledge and offer new ways to study RNA structure and function. In particular, attention-based transformer models are effective at capturing both short- and long-distance relationships, making them well-suited for modeling complex RNA sequences. This review highlights recent advances in RNA secondary structure prediction using transformer-based approaches, focusing on key models such as E2EFold, ATTFold, RNAformer, and DEBFold. It also discusses current challenges, future research directions, and the impact of attention-based deep learning on the field of RNA structural bioinformatics.

 © 2025 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 RNA structure, Deep learning, Transformer models, Thermodynamic methods, Pseudoknot prediction

 Article history

 Received 1 March 2025, Received in revised form 25 July 2025, Accepted 7 August 2025

 Acknowledgment

The authors would like to thank the Ministry of Higher Education for providing financial support under the Fundamental Research Grant Scheme (FRGS) (FRGS/1/2021/TKO/UNIMAP/02/65). 

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Askar MNA, Abdullah AA, Mashor MY, Mohamed-Hussein Z-A, Mohamed Z, Ang WC, and Kanaya S (2025). Deep learning and attention mechanisms in RNA secondary structure prediction: A critical survey. International Journal of Advanced and Applied Sciences, 12(9): 61-78

  Permanent Link to this page

 Figures

  Fig. 1   Fig. 2   Fig. 3   Fig. 4   Fig. 5   Fig. 6

 Tables

  Table 1   Table 2   Table 3   

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