From sequence to structure: A comprehensive review of deep learning models for RNA structure prediction

Key Points

• RNA structure prediction has evolved with deep learning techniques, providing improved accuracy over traditional methods.
• Neural networks are at the core of modern approaches, effectively learning complex patterns in RNA sequences and structures.
• These models leverage large datasets of RNA sequences and known structures to enhance the prediction process significantly.
• The review emphasizes the need for ongoing improvement in model architecture to address limitations in current RNA prediction methods.