ArticlePLoS computational biology2023
Sequence similarity governs generalizability of de novo deep learning models for RNA secondary structure prediction.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Folding the message: mRNA structure as a regulatory layer of human mitochondrial gene expression.Biochimica et biophysica acta. Molecular cell research · 2026Review
- NTFold: Structure-Sensing Nucleotide Attention Learning for RNA Secondary Structure Prediction.Sensors (Basel, Switzerland) · 2026Article
- Designing molecular RNA switches with Restricted Boltzmann machines.Nature communications · 2025Article
- Decoding the interactions and functions of non-coding RNA with artificial intelligence.Nature reviews. Molecular cell biology · 2025Review
- Deep generalizable prediction of RNA secondary structure via base pair motif energy.Nature communications · 2025Article
- Benchmarking the methods for predicting base pairs in RNA-RNA interactions.Bioinformatics (Oxford, England) · 2025Article
- Review
- Robust RNA secondary structure prediction with a mixture of deep learning and physics-based experts.Biology methods & protocols · 2025Article
- RNA3DB: A structurally-dissimilar dataset split for training and benchmarking deep learning models for RNA structure prediction.Journal of molecular biology · 2024Article
- Accurate prediction of RNA secondary structure including pseudoknots through solving minimum-cost flow with learned potentials.Communications biology · 2024Article
- Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery.Briefings in bioinformatics · 2023Review
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Abstract
Making no use of physical laws or co-evolutionary information, de novo deep learning (DL) models for RNA secondary structure prediction have achieved far superior performances than traditional algorithms. However, their statistical underpinning raises the crucial question of generalizability. We present a quantitative study of the performance and generalizability of a series of de novo DL models, with a minimal two-module architecture and no post-processing, under varied similarities between seen and unseen sequences. Our models demonstrate excellent expressive capacities and outperform existing methods on common benchmark datasets. However, model generalizability, i.e., the performance gap between the seen and unseen sets, degrades rapidly as the sequence similarity decreases. The same trends are observed from several recent DL and machine learning models. And an inverse correlation between performance and generalizability is revealed collectively across all learning-based models with wide-ranging architectures and sizes. We further quantitate how generalizability depends on sequence and structure identity scores via pairwise alignment, providing unique quantitative insights into the limitations of statistical learning. Generalizability thus poses a major hurdle for deploying de novo DL models in practice and various pathways for future advances are discussed.
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