[Research progress in RNA secondary structure prediction methods]

As an essential aspect of structural biology, RNA secondary structures play a crucial role in maintaining molecular stability and regulating various biological functions. Since experimental approaches for resolving RNA secondary structures are often complex and costly, developing efficient and accurate prediction methods has become a key direction in RNA structural research. Computational biology, an important analytical tool, provides structural biology with theoretical foundations and algorithmic support. Due to its great potential in RNA secondary structure prediction, it has attracted extensive attention in biomedical research. This article first outlined the main computational methods for RNA secondary structure prediction, including energy-based methods, multiple-sequence methods, traditional machine learning methods, deep learning methods, and tertiary structure-based RNA secondary structure prediction methods and compared the advantages and disadvantages of various algorithms. Then, this article discussed the applications of related techniques in biomedical fields, with a particular focus on the identification of RNA-binding protein sites. Finally, it provided an outlook on the future development of RNA secondary structure prediction methods. This review is expected to provide important references for relevant research.