ArticleNature communications2021
RNA secondary structure prediction using deep learning with thermodynamic integration.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 226 papers.
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Who cites it
226 citing papers in PubMed.
- From RNA design to delivery: Computational strategies for functional RNA therapeutics.Bioactive materials · 2027Review
- The mRNA architecture of the translation termination site primes programmed stop codon readthrough events inRNA biology · 2026Article
- Crosstalk between RNA secondary and three-dimensional structure prediction: a comprehensive study.RNA biology · 2026Article
- Reading the SINEs - functionality in noncoding RNA 'cheap genes'.RNA biology · 2026Review
- A common germline 3' UTR variant controls DDIT4 translation efficiency and stress responses in cancer cells.iScience · 2026Article
- Mitogenome of Rhabdophis swinhonis (Squamata: Colubridae): Its Comparative Description and Phylogenetic Position Within Colubridae.Biochemical genetics · 2026Article
- 4MRNA: a new approach for nucleic acid molecular replacement using models with diverse parameter patterns.RNA (New York, N.Y.) · 2026Article
- UFold-X: an enhanced Dual & Dynamic U-Mamba model for long-range RNA secondary structure prediction.Nucleic acids research · 2026Article
- Multiheme selenoenzyme essential for elemental sulfur respiration.Science advances · 2026Article
- ZBP1 in Neuroinflammation and Neurodegeneration: Z-Nucleic-Acid Sensing, RHIM Signalling and Therapeutic Targeting.International journal of molecular sciences · 2026Review
- GC content mismatch of transgene destabilizes RNA virus genomes.Journal of virology · 2026Article
- Tree_RNA-Align: RNA Secondary Structure Clustering and Classification Based on Tree-Structure Alignment.International journal of molecular sciences · 2026Article
- Mitochondrial translation termination, recycling, reinitiation, and rescue for in-frame and out-of-frame contexts.Nature communications · 2026Article
- Dual-functional nanoplatform for simultaneous degradation of circRNAJournal of Zhejiang University. Science. B · 2026Article
- Splice-altering TP53 missense mutations as drivers of dysfunction and targets for RNA-based therapy.Stem cell research & therapy · 2026Article
- ShapeRNA: an integrated web server for RNA secondary structure, ensemble, and functional analysis.Nucleic acids research · 2026Article
- When does additional information improve accuracy of RNA secondary structure prediction?bioRxiv : the preprint server for biology · 2026Article
- PlanarFold: a coarse-grained molecular dynamics model of RNA in two-dimensional space.Nature communications · 2026Article
- Employing molecular beacons to assess in vitro transcription with single-molecule resolution.Scientific reports · 2026Article
- Article
166 more citing papers are in PubMed but not listed here.
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3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate predictions of RNA secondary structures can help uncover the roles of functional non-coding RNAs. Although machine learning-based models have achieved high performance in terms of prediction accuracy, overfitting is a common risk for such highly parameterized models. Here we show that overfitting can be minimized when RNA folding scores learnt using a deep neural network are integrated together with Turner's nearest-neighbor free energy parameters. Training the model with thermodynamic regularization ensures that folding scores and the calculated free energy are as close as possible. In computational experiments designed for newly discovered non-coding RNAs, our algorithm (MXfold2) achieves the most robust and accurate predictions of RNA secondary structures without sacrificing computational efficiency compared to several other algorithms. The results suggest that integrating thermodynamic information could help improve the robustness of deep learning-based predictions of RNA secondary structure.
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