ArticleNature communications2019
RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 183 papers.
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Who cites it
183 citing papers in PubMed.
- Crosstalk between RNA secondary and three-dimensional structure prediction: a comprehensive study.RNA biology · 2026Article
- The trRosettaRNA server for RNA structure prediction.Nature protocols · 2026Review
- Long-range mRNA folding shapes expression and sequence of bacterial genes.Molecular cell · 2026Article
- Novel Non-Coding Small sRNA002 Targets the Virulence-Related GenesMicroorganisms · 2026Article
- UFold-X: an enhanced Dual & Dynamic U-Mamba model for long-range RNA secondary structure prediction.Nucleic acids research · 2026Article
- RNAbpFlow: base pair-augmented SE(3) flow matching for conditional RNA 3D structure generation.Nature methods · 2026Article
- PlanarFold: a coarse-grained molecular dynamics model of RNA in two-dimensional space.Nature communications · 2026Article
- Bridging technical innovation and computational advances in studies of RNA-protein assemblies.Nature reviews. Genetics · 2026Review
- An RNA Language Model trained on sequence alone reveals the structural logic of Internal Ribosome Entry Sites.bioRxiv : the preprint server for biology · 2026Article
- Exploring Secondary Structure Predictions for RNA-Targeted Drug Discovery: Power and Challenges.Journal of chemical information and modeling · 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
- Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning.Briefings in bioinformatics · 2026Article
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Long non-coding RNAs in B-cell acute lymphoblastic leukemia: Disease implication, challenges and therapeutic opportunities.Non-coding RNA research · 2026Review
- The role of low-complexity repeats in RNA-RNA interactions and a deep learning framework for duplex prediction.Nature communications · 2026Article
- NTFold: Structure-Sensing Nucleotide Attention Learning for RNA Secondary Structure Prediction.Sensors (Basel, Switzerland) · 2026Article
- TiRNA: a coarse-grained method with temperature and ion effects for RNA structure folding and prediction.Nucleic acids research · 2026Article
- Predicting small molecule-RNA interactions without RNA tertiary structures.Nature biotechnology · 2026Article
- AI-powered mapping of tumor immunity for optimized mRNA vaccine engineering.Frontiers in oncology · 2026Review
123 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The majority of our human genome transcribes into noncoding RNAs with unknown structures and functions. Obtaining functional clues for noncoding RNAs requires accurate base-pairing or secondary-structure prediction. However, the performance of such predictions by current folding-based algorithms has been stagnated for more than a decade. Here, we propose the use of deep contextual learning for base-pair prediction including those noncanonical and non-nested (pseudoknot) base pairs stabilized by tertiary interactions. Since only [Formula: see text]250 nonredundant, high-resolution RNA structures are available for model training, we utilize transfer learning from a model initially trained with a recent high-quality bpRNA dataset of [Formula: see text]10,000 nonredundant RNAs made available through comparative analysis. The resulting method achieves large, statistically significant improvement in predicting all base pairs, noncanonical and non-nested base pairs in particular. The proposed method (SPOT-RNA), with a freely available server and standalone software, should be useful for improving RNA structure modeling, sequence alignment, and functional annotations.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.