ArticleRNA (New York, N.Y.)2010
Computational approaches for RNA energy parameter estimation.
Article in RNA (New York, N.Y.), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.
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Who cites it
63 citing papers in PubMed.
- RNA Folding Nearest Neighbor Parameters Including the Modification 1-Methyl-Pseudouridine.bioRxiv : the preprint server for biology · 2026Article
- Machine learning for RNA secondary structure prediction: a review of current methods and challenges.RNA (New York, N.Y.) · 2026Review
- Deep learning for RNA secondary structure determination: gauging generalizability and broadening the scope of traditional methods.RNA (New York, N.Y.) · 2026Review
- Deep Learning for RNA Secondary Structure Determination: Gauging Generalizability and Broadening the Scope of Traditional Methods.bioRxiv : the preprint server for biology · 2025Article
- Review
- RNAbpFlow: Base pair-augmented SE(3)-flow matching for conditional RNA 3D structure generation.bioRxiv : the preprint server for biology · 2025Article
- RiNALMo: general-purpose RNA language models can generalize well on structure prediction tasks.Nature communications · 2025Article
- Molecular and morphological characterisation of Gyrodactylus alviga (Monogenea: Gyrodactylidae) from the Black Sea, and its synonymisation with G. pterygialis from the North European seas.Systematic parasitology · 2025Article
- Predicting RNA sequence-structure likelihood via structure-aware deep learning.BMC bioinformatics · 2024Article
- NNDB: An Expanded Database of Nearest Neighbor Parameters for Predicting Stability of Nucleic Acid Secondary Structures.Journal of molecular biology · 2024Article
- LinearAlifold: Linear-time consensus structure prediction for RNA alignments.Journal of molecular biology · 2024Article
- RNA3DB: A structurally-dissimilar dataset split for training and benchmarking deep learning models for RNA structure prediction.Journal of molecular biology · 2024Article
- Estimating RNA Secondary Structure Folding Free Energy Changes with efn2.Methods in molecular biology (Clifton, N.J.) · 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
- Genome-Wide RNA Secondary Structure Prediction.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Fast RNA-RNA Interaction Prediction Methods for Interaction Analysis of Transcriptome-Scale Large Datasets.Methods in molecular biology (Clifton, N.J.) · 2023Review
- RNA Secondary Structure Prediction Based on Energy Models.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Article
- Advances and opportunities in RNA structure experimental determination and computational modeling.Nature methods · 2022Review
- RNA secondary structure packages evaluated and improved by high-throughput experiments.Nature methods · 2022Article
3 more citing papers are in PubMed but not listed here.
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5 authors.
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Abstract
Methods for efficient and accurate prediction of RNA structure are increasingly valuable, given the current rapid advances in understanding the diverse functions of RNA molecules in the cell. To enhance the accuracy of secondary structure predictions, we developed and refined optimization techniques for the estimation of energy parameters. We build on two previous approaches to RNA free-energy parameter estimation: (1) the Constraint Generation (CG) method, which iteratively generates constraints that enforce known structures to have energies lower than other structures for the same molecule; and (2) the Boltzmann Likelihood (BL) method, which infers a set of RNA free-energy parameters that maximize the conditional likelihood of a set of reference RNA structures. Here, we extend these approaches in two main ways: We propose (1) a max-margin extension of CG, and (2) a novel linear Gaussian Bayesian network that models feature relationships, which effectively makes use of sparse data by sharing statistical strength between parameters. We obtain significant improvements in the accuracy of RNA minimum free-energy pseudoknot-free secondary structure prediction when measured on a comprehensive set of 2518 RNA molecules with reference structures. Our parameters can be used in conjunction with software that predicts RNA secondary structures, RNA hybridization, or ensembles of structures. Our data, software, results, and parameter sets in various formats are freely available at http://www.cs.ubc.ca/labs/beta/Projects/RNA-Params.
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