ArticleBMC bioinformatics2022
RNA secondary structure prediction with convolutional neural networks.
Article in BMC bioinformatics, 2022. 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.
- An efficient algorithm for exploring RNA branching conformations under the nearest-neighbor thermodynamic model.Algorithms for molecular biology : AMB · 2026Article
- Machine learning for RNA secondary structure prediction: a review of current methods and challenges.RNA (New York, N.Y.) · 2026Review
- Inferring fungal cis-regulatory networks from genome sequences via unsupervised and interpretable representation learning.Genetics · 2026Article
- Hierarchical analysis of RNA secondary structures with pseudoknots based on sections.PLoS computational biology · 2026Article
- Energy-Dependent Changes in Ion Mobility Peak Width Reveal Stability Shifts in Ribonucleic Acid Stem Loops.Journal of the American Society for Mass Spectrometry · 2025Article
- Unraveling unbreakable hairpins: characterizing RNA secondary structures that are persistent after dinucleotide shuffling.RNA (New York, N.Y.) · 2025Article
- mRNA vaccine sequence and structure design and optimization: Advances and challenges.The Journal of biological chemistry · 2025Review
- A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: Focus on pseudoknots identification.PloS one · 2025Article
- Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence.Molecules (Basel, Switzerland) · 2024Review
- Machine learning in RNA structure prediction: Advances and challenges.Biophysical journal · 2024Review
- Graph Convolutional Network for predicting secondary structure of RNA.Research square · 2024Article
- Article
- Inverse folding based pre-training for the reliable identification of intrinsic transcription terminators.PLoS computational biology · 2022Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting the secondary, i.e. base-pairing structure of a folded RNA strand is an important problem in synthetic and computational biology. First-principle algorithmic approaches to this task are challenging because existing models of the folding process are inaccurate, and even if a perfect model existed, finding an optimal solution would be in general NP-complete.
resultsIn this paper, we propose a simple, yet effective data-driven approach. We represent RNA sequences in the form of three-dimensional tensors in which we encode possible relations between all pairs of bases in a given sequence. We then use a convolutional neural network to predict a two-dimensional map which represents the correct pairings between the bases. Our model achieves significant accuracy improvements over existing methods on two standard datasets, RNAStrAlign and ArchiveII, for 10 RNA families, where our experiments show excellent performance of the model across a wide range of sequence lengths. Since our matrix representation and post-processing approaches do not require the structures to be pseudoknot-free, we get similar good performance also for pseudoknotted structures.
conclusionWe show how to use an artificial neural network design to predict the structure for a given RNA sequence with high accuracy only by learning from samples whose native structures have been experimentally characterized, independent of any energy model.
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