RNA structure prediction using deep learning — A comprehensive review

In computational biology, accurate RNA structure prediction offers several benefits, including facilitating a better understanding of RNA functions and RNA-based drug design. Implementing deep learning techniques for RNA structure prediction has led tremendous progress in this field, resulting in si...

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Vydáno v:Computers in biology and medicine Ročník 188; s. 109845
Hlavní autoři: Chaturvedi, Mayank, Rashid, Mahmood A., Paliwal, Kuldip K.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Elsevier Ltd 01.04.2025
Elsevier Limited
Témata:
Artificial intelligence
Comparative analysis
Computational Biology - methods
Deep Learning
Drug development
Feature extraction
Humans
Internal Medicine
Machine learning
Neural networks
Nucleic Acid Conformation
Other
Predictions
Reviews
Ribonucleic acid
RNA
RNA - chemistry
RNA - genetics
RNA secondary structure prediction
Transformers
Deep learning
Transformers
Feature extraction
Neural networks
RNA secondary structure prediction
Machine learning
ISSN:0010-4825, 1879-0534, 1879-0534
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Shrnutí:In computational biology, accurate RNA structure prediction offers several benefits, including facilitating a better understanding of RNA functions and RNA-based drug design. Implementing deep learning techniques for RNA structure prediction has led tremendous progress in this field, resulting in significant improvements in prediction accuracy. This comprehensive review aims to provide an overview of the diverse strategies employed in predicting RNA secondary structures, emphasizing deep learning methods. The article categorizes the discussion into three main dimensions: feature extraction methods, existing state-of-the-art learning model architectures, and prediction approaches. We present a comparative analysis of various techniques and models highlighting their strengths and weaknesses. Finally, we identify gaps in the literature, discuss current challenges, and suggest future approaches to enhance model performance and applicability in RNA structure prediction tasks. This review provides a deeper insight into the subject and paves the way for further progress in this dynamic intersection of life sciences and artificial intelligence. [Display omitted] •Critically reviews deep learning methods for RNA secondary structure prediction.•Highlights the recent progresses in deep learning methods for structure prediction.•Explores key challenges in RNA secondary structure prediction using deep learning.•Recommends future directions for deep learning-driven RNA structure prediction.
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ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2025.109845