This review surveys current machine learning methods for RNA secondary structure prediction, identifies a generalization crisis prompting homology-aware benchmarking, and outlines future challenges including pseudoknots, long transcripts, modified nucleotides, and dynamic ensembles.
L., Fontana, W., Stadler, P
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Machine Learning for RNA Secondary Structure Prediction: a review of current methods and challenges
This review surveys current machine learning methods for RNA secondary structure prediction, identifies a generalization crisis prompting homology-aware benchmarking, and outlines future challenges including pseudoknots, long transcripts, modified nucleotides, and dynamic ensembles.