Review of machine learning methods for RNA secondary structure prediction.

Area of Science:

• Molecular Biology
• Bioinformatics
• Computational Biology

Background:

• The function of noncoding RNAs is intrinsically linked to their secondary structure.
• Accurate identification of RNA secondary structures is vital for biological research.
• Computational prediction methods have historically dominated RNA structure analysis.

Purpose of the Study:

• To provide a comprehensive review of machine learning (ML)-based RNA secondary structure prediction methods.
• To summarize key ML approaches and their performance in the field.
• To discuss current challenges and future directions in RNA secondary structure prediction.

Main Methods:

• Review of existing literature on RNA secondary structure prediction.
• Focus on methods utilizing machine learning (ML) and deep learning (DL) technologies.
• Tabularized summary of prominent ML-based prediction methods.

Main Results:

• Machine learning, especially deep learning, has shown promise in overcoming performance stagnation in RNA secondary structure prediction.
• Recent advances in RNA structure data availability have fueled ML-based method development.
• The review consolidates information on various ML techniques applied to this problem.

Conclusions:

• ML technologies represent a significant advancement in RNA secondary structure prediction.
• Addressing current challenges and exploring future trends will further enhance prediction accuracy and utility.
• This review serves as a valuable resource for researchers in the field.