ArticleBMC bioinformatics2010
Prediction of RNA secondary structure by maximizing pseudo-expected accuracy.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- ConsAlign: simultaneous RNA structural aligner based on rich transfer learning and thermodynamic ensemble model of alignment scoring.Bioinformatics (Oxford, England) · 2023Article
- Prediction of RNA secondary structure including pseudoknots for long sequences.Briefings in bioinformatics · 2022Article
- LinAliFold and CentroidLinAliFold: fast RNA consensus secondary structure prediction for aligned sequences using beam search methods.Bioinformatics advances · 2022Article
- RNA Secondary Structures with Limited Base Pair Span: Exact Backtracking and an Application.Genes · 2020Article
- RNA genome conservation and secondary structure in SARS-CoV-2 and SARS-related viruses: a first look.RNA (New York, N.Y.) · 2020Article
- RNA genome conservation and secondary structure in SARS-CoV-2 and SARS-related viruses.bioRxiv : the preprint server for biology · 2020Article
- Comparative and integrative analysis of RNA structural profiling data: current practices and emerging questions.Quantitative biology (Beijing, China) · 2017Article
- Long non-coding RNA and alternative splicing modulations in Parkinson's leukocytes identified by RNA sequencing.PLoS computational biology · 2014Article
- CompaRNA: a server for continuous benchmarking of automated methods for RNA secondary structure prediction.Nucleic acids research · 2013Article
- A classification of bioinformatics algorithms from the viewpoint of maximizing expected accuracy (MEA).Journal of computational biology : a journal of computational molecular cell biology · 2012Review
- Analysis of energy-based algorithms for RNA secondary structure prediction.BMC bioinformatics · 2012Article
- IPknot: fast and accurate prediction of RNA secondary structures with pseudoknots using integer programming.Bioinformatics (Oxford, England) · 2011Article
- Generalized centroid estimators in bioinformatics.PloS one · 2011Article
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3 authors.
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Abstract
backgroundRecent studies have revealed the importance of considering the entire distribution of possible secondary structures in RNA secondary structure predictions; therefore, a new type of estimator is proposed including the maximum expected accuracy (MEA) estimator. The MEA-based estimators have been designed to maximize the expected accuracy of the base-pairs and have achieved the highest level of accuracy. Those methods, however, do not give the single best prediction of the structure, but employ parameters to control the trade-off between the sensitivity and the positive predictive value (PPV). It is unclear what parameter value we should use, and even the well-trained default parameter value does not, in general, give the best result in popular accuracy measures to each RNA sequence.
resultsInstead of using the expected values of the popular accuracy measures for RNA secondary structure prediction, which is difficult to be calculated, the pseudo-expected accuracy, which can easily be computed from base-pairing probabilities, is introduced. It is shown that the pseudo-expected accuracy is a good approximation in terms of sensitivity, PPV, MCC, or F-score. The pseudo-expected accuracy can be approximately maximized for each RNA sequence by stochastic sampling. It is also shown that well-balanced secondary structures between sensitivity and PPV can be predicted with a small computational overhead by combining the pseudo-expected accuracy of MCC or F-score with the γ-centroid estimator.
conclusionsThis study gives not only a method for predicting the secondary structure that balances between sensitivity and PPV, but also a general method for approximately maximizing the (pseudo-)expected accuracy with respect to various evaluation measures including MCC and F-score.
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