Machine learning for RNA secondary structure prediction: a review of current methods and challenges

Area of Science:

• Computational Biology
• Molecular Biology
• Bioinformatics

Background:

• RNA secondary structure prediction is vital for understanding RNA function and developing therapeutics.
• Traditional thermodynamic models have limitations; machine learning (ML) and deep learning (DL) now dominate, offering improved accuracy.
• The field faces a
• generalization crisis,
• where ML models struggle with novel RNA families, necessitating robust benchmarking.

Purpose of the Study:

• To review modern machine learning and deep learning methods for RNA secondary structure prediction.
• To discuss the challenges and advancements in the field, including the generalization crisis and data scarcity.
• To explore future directions, such as predicting complex motifs, longer transcripts, and dynamic RNA ensembles.

Main Methods:

• Survey of single-sequence, evolutionary-based, and hybrid ML/biophysics models.
• Analysis of the impact of homology-aware benchmarking on model evaluation.
• Introduction of RNA foundation models trained on large unlabeled sequence data.

Main Results:

• Machine learning and deep learning models have significantly advanced RNA secondary structure prediction accuracy.
• The generalization crisis highlights the need for improved model robustness and rigorous evaluation.
• RNA foundation models show potential to overcome data scarcity and enhance generalization.

Conclusions:

• The field is shifting towards data-driven approaches, emphasizing generalization and robust benchmarking.
• Future research must address complex RNA structures, modified nucleotides, and dynamic behavior.
• Standardized prospective benchmarking is essential for validating progress in RNA structure prediction.