ArticleRNA (New York, N.Y.)2012
A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more.
Article in RNA (New York, N.Y.), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers.
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Who cites it
71 citing papers in PubMed.
- Markov models of SHAPE data improve secondary structure prediction.bioRxiv : the preprint server for biology · 2026Article
- UFold-X: an enhanced Dual & Dynamic U-Mamba model for long-range RNA secondary structure prediction.Nucleic acids research · 2026Article
- ShapeRNA: an integrated web server for RNA secondary structure, ensemble, and functional analysis.Nucleic acids research · 2026Article
- When does additional information improve accuracy of RNA secondary structure prediction?bioRxiv : the preprint server for biology · 2026Article
- Exploring Secondary Structure Predictions for RNA-Targeted Drug Discovery: Power and Challenges.Journal of chemical information and modeling · 2026Article
- What does it take to learn the rules of RNA base pairing? A lot less than you may think.Communications biology · 2026Article
- Deep learning for RNA secondary structure determination: gauging generalizability and broadening the scope of traditional methods.RNA (New York, N.Y.) · 2026Review
- Machine learning for RNA secondary structure prediction: a review of current methods and challenges.RNA (New York, N.Y.) · 2026Review
- AI-powered mapping of tumor immunity for optimized mRNA vaccine engineering.Frontiers in oncology · 2026Review
- Machine Learning Approaches for Predicting RNA-RNA/DNA Interactions.Methods in molecular biology (Clifton, N.J.) · 2026Review
- Designing molecular RNA switches with Restricted Boltzmann machines.Nature communications · 2025Article
- ERNIE-RNA: an RNA language model with structure-enhanced representations.Nature communications · 2025Article
- Deep Learning for RNA Secondary Structure Determination: Gauging Generalizability and Broadening the Scope of Traditional Methods.bioRxiv : the preprint server for biology · 2025Article
- All-at-once RNA folding with 3D motif prediction framed by evolutionary information.Nature methods · 2025Article
- Enhanced Generalizability of RNA Secondary Structure Prediction via Convolutional Block Attention Network and Ensemble Learning.Molecules (Basel, Switzerland) · 2025Article
- ECSFinder: optimized prediction of evolutionarily conserved RNA secondary structures from genome sequences.Nucleic acids research · 2025Article
- AlignmentFold and AlignmentPartition: Improving the align-then-fold approach for RNA secondary structure prediction.bioRxiv : the preprint server for biology · 2025Article
- RiNALMo: general-purpose RNA language models can generalize well on structure prediction tasks.Nature communications · 2025Article
- Deep generalizable prediction of RNA secondary structure via base pair motif energy.Nature communications · 2025Article
- bpRNA-CosMoS: a robust and efficient RNA structural comparison method using k-mer based cosine similarity.Bioinformatics (Oxford, England) · 2025Article
11 more citing papers are in PubMed but not listed here.
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3 authors.
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Abstract
The standard approach for single-sequence RNA secondary structure prediction uses a nearest-neighbor thermodynamic model with several thousand experimentally determined energy parameters. An attractive alternative is to use statistical approaches with parameters estimated from growing databases of structural RNAs. Good results have been reported for discriminative statistical methods using complex nearest-neighbor models, including CONTRAfold, Simfold, and ContextFold. Little work has been reported on generative probabilistic models (stochastic context-free grammars [SCFGs]) of comparable complexity, although probabilistic models are generally easier to train and to use. To explore a range of probabilistic models of increasing complexity, and to directly compare probabilistic, thermodynamic, and discriminative approaches, we created TORNADO, a computational tool that can parse a wide spectrum of RNA grammar architectures (including the standard nearest-neighbor model and more) using a generalized super-grammar that can be parameterized with probabilities, energies, or arbitrary scores. By using TORNADO, we find that probabilistic nearest-neighbor models perform comparably to (but not significantly better than) discriminative methods. We find that complex statistical models are prone to overfitting RNA structure and that evaluations should use structurally nonhomologous training and test data sets. Overfitting has affected at least one published method (ContextFold). The most important barrier to improving statistical approaches for RNA secondary structure prediction is the lack of diversity of well-curated single-sequence RNA secondary structures in current RNA databases.
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