# A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more.

@article{Rivas2012ARO, title={A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more.}, author={Elena Rivas and Raymond W. Lang and Sean R. Eddy}, journal={RNA}, year={2012}, volume={18 2}, pages={ 193-212 } }

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…

## 86 Citations

### Evaluation of a sophisticated SCFG design for RNA secondary structure prediction

- Computer ScienceTheory in Biosciences
- 2011

Investigations on both the accuracies of predicted foldings and the overall quality of generated sample sets yield the conclusion that the Boltzmann distribution of the PF sampling approach is more centered than the ensemble distribution induced by the sophisticated SCFG model, which implies a greater structural diversity within generated samples.

### Improving RNA Branching Predictions: Advances and Limitations

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A branch-and-bound algorithm is developed that finds the set of optimal parameters with the highest average accuracy for a given set of sequences and shows that the previous ad hoc parameters are nearly optimal for tRNA and 5S rRNA sequences on both training and testing sets.

### A semi-supervised learning approach for RNA secondary structure prediction

- Computer Science, BiologyComput. Biol. Chem.
- 2015

### A max-margin training of RNA secondary structure prediction integrated with the thermodynamic model

- Computer SciencebioRxiv
- 2017

A novel algorithm for RNA secondary structure prediction that integrates the thermodynamic approach and the machine learning based weighted approach is proposed that achieves the best prediction accuracy compared with existing methods, and heavy overfitting cannot be observed.

### Analysis of RNA nearest neighbor parameters reveals interdependencies and quantifies the uncertainty in RNA secondary structure prediction.

- PhysicsRNA
- 2018

This work demonstrated that the precision of RNA secondary structure prediction is more robust than suggested by previous work based on perturbation of the nearest neighbor parameters, due to correlations between parameters.

### A Test and Refinement of Folding Free Energy Nearest Neighbor Parameters for RNA Including N6-Methyladenosine.

- BiologyJournal of molecular biology
- 2022

### The four ingredients of single-sequence RNA secondary structure prediction. A unifying perspective

- Computer ScienceRNA biology
- 2013

Modeling RNA secondary structure by using intrinsic sequence-based plausible “foldability” will require the incorporation of other forms of information in order to constrain the folding space and to improve prediction accuracy, which could give an advantage to probabilistic scoring systems.

### Stochastic k-Tree Grammar and Its Application in Biomolecular Structure Modeling

- Computer ScienceLATA
- 2014

It is shown, for the first time, that probabilistic analysis of k-trees over strings are computable in polynomial time n Ok, which permits not only modeling of biomolecular tertiary structures but also efficient analysis and prediction of such structures.

### RNA secondary structure prediction using deep learning with thermodynamic integration

- Computer SciencebioRxiv
- 2020

A new algorithm for predicting RNA secondary structures that uses deep learning with thermodynamic integration, thereby enabling robust predictions and a new regularization for training the authors' deep neural network without overfitting it to the training data.

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