ArticleMolecules (Basel, Switzerland)2022
Length-Dependent Deep Learning Model for RNA Secondary Structure Prediction.
Article in Molecules (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Review
- Benchmarking the methods for predicting base pairs in RNA-RNA interactions.Bioinformatics (Oxford, England) · 2025Article
- RNA-Puzzles Round V: blind predictions of 23 RNA structures.Nature methods · 2025Article
- Robust RNA secondary structure prediction with a mixture of deep learning and physics-based experts.Biology methods & protocols · 2025Article
- mRNA vaccine sequence and structure design and optimization: Advances and challenges.The Journal of biological chemistry · 2025Review
- Machine learning in RNA structure prediction: Advances and challenges.Biophysical journal · 2024Review
- Sequence similarity governs generalizability of de novo deep learning models for RNA secondary structure prediction.PLoS computational biology · 2023Article
- Examples of Structural Motifs in Viral Genomes and Approaches for RNA Structure Characterization.International journal of molecular sciences · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Deep learning methods for RNA secondary structure prediction have shown higher performance than traditional methods, but there is still much room to improve. It is known that the lengths of RNAs are very different, as are their secondary structures. However, the current deep learning methods all use length-independent models, so it is difficult for these models to learn very different secondary structures. Here, we propose a length-dependent model that is obtained by further training the length-independent model for different length ranges of RNAs through transfer learning. 2dRNA, a coupled deep learning neural network for RNA secondary structure prediction, is used to do this. Benchmarking shows that the length-dependent model performs better than the usual length-independent model.
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Registered trials
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