ArticleBMC bioinformatics2019
Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter.
Article in BMC bioinformatics, 2019. 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.
- Transformers in RNA structure prediction: A review.Computational and structural biotechnology journal · 2025Review
- Predicting circRNA-miRNA interactions utilizing transformer-based RNA sequential learning and high-order proximity preserved embedding.iScience · 2024Article
- Machine learning for RNA 2D structure prediction benchmarked on experimental data.Briefings in bioinformatics · 2023Review
- Sequence similarity governs generalizability of de novo deep learning models for RNA secondary structure prediction.PLoS computational biology · 2023Article
- RNA independent fragment partition method based on deep learning for RNA secondary structure prediction.Scientific reports · 2023Article
- Review
- Inverse folding based pre-training for the reliable identification of intrinsic transcription terminators.PLoS computational biology · 2022Article
- RNA folding using quantum computers.PLoS computational biology · 2022Article
- Research on RNA secondary structure predicting via bidirectional recurrent neural network.BMC bioinformatics · 2021Article
- Review of machine learning methods for RNA secondary structure prediction.PLoS computational biology · 2021Review
- Identifying Membrane Protein Types Based on Lifelong Learning With Dynamically Scalable Networks.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRNA secondary structure prediction is an important issue in structural bioinformatics, and RNA pseudoknotted secondary structure prediction represents an NP-hard problem. Recently, many different machine-learning methods, Markov models, and neural networks have been employed for this problem, with encouraging results regarding their predictive accuracy; however, their performances are usually limited by the requirements of the learning model and over-fitting, which requires use of a fixed number of training features. Because most natural biological sequences have variable lengths, the sequences have to be truncated before the features are employed by the learning model, which not only leads to the loss of information but also destroys biological-sequence integrity.
resultsTo address this problem, we propose an adaptive sequence length based on deep-learning model and integrate an energy-based filter to remove the over-fitting base pairs.
conclusionsComparative experiments conducted on an authoritative dataset RNA STRAND (RNA secondary STRucture and statistical Analysis Database) revealed a 12% higher accuracy relative to three currently used methods.
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