Machine learning in RNA structure prediction: Advances and challenges.
Biophys J
; 123(17): 2647-2657, 2024 Sep 03.
Article
em En
| MEDLINE
| ID: mdl-38297836
ABSTRACT
RNA molecules play a crucial role in various biological processes, with their functionality closely tied to their structures. The remarkable advancements in machine learning techniques for protein structure prediction have shown promise in the field of RNA structure prediction. In this perspective, we discuss the advances and challenges encountered in constructing machine learning-based models for RNA structure prediction. We explore topics including model building strategies, specific challenges involved in predicting RNA secondary (2D) and tertiary (3D) structures, and approaches to these challenges. In addition, we highlight the advantages and challenges of constructing RNA language models. Given the rapid advances of machine learning techniques, we anticipate that machine learning-based models will serve as important tools for predicting RNA structures, thereby enriching our understanding of RNA structures and their corresponding functions.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
RNA
/
Aprendizado de Máquina
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Conformação de Ácido Nucleico
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article