ReviewPLoS computational biology2021
Review of machine learning methods for RNA secondary structure prediction.
Review in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
42 citing papers in PubMed.
- When does additional information improve accuracy of RNA secondary structure prediction?bioRxiv : the preprint server for biology · 2026Article
- An efficient algorithm for exploring RNA branching conformations under the nearest-neighbor thermodynamic model.Algorithms for molecular biology : AMB · 2026Article
- NTFold: Structure-Sensing Nucleotide Attention Learning for RNA Secondary Structure Prediction.Sensors (Basel, Switzerland) · 2026Article
- Computational Ligand-Binding Site Prediction.Advances in experimental medicine and biology · 2026Review
- Review
- Enhanced Generalizability of RNA Secondary Structure Prediction via Convolutional Block Attention Network and Ensemble Learning.Molecules (Basel, Switzerland) · 2025Article
- Improving RNA Secondary Structure Prediction Through Expanded Training Data.bioRxiv : the preprint server for biology · 2025Article
- High-throughput DNA melt measurements enable improved models of DNA folding thermodynamics.Nature communications · 2025Article
- Comprehensive datasets for RNA design, machine learning, and beyond.Scientific reports · 2025Article
- Enhanced RNA secondary structure prediction through integrative deep learning and structural context analysis.Nucleic acids research · 2025Article
- Non-transgenic RNAi strategies for sustainable plant viral disease control: a review.Molecular biology reports · 2025Review
- DRAG: design RNAs as hierarchical graphs with reinforcement learning.Briefings in bioinformatics · 2025Article
- The Unpaved Road of Non-Coding RNA Structure-Function Relationships: Current Knowledge, Available Methodologies, and Future Trends.Non-coding RNA · 2025Review
- Robust RNA secondary structure prediction with a mixture of deep learning and physics-based experts.Biology methods & protocols · 2025Article
- Effects of Molecular Crowding on the Structure, Folding, Stability, and Catalysis of RNA.Sub-cellular biochemistry · 2025Review
- StructRMDB: A database of RNA modification sites that affect RNA secondary structure.Computational and structural biotechnology journal · 2025Article
- Transformers in RNA structure prediction: A review.Computational and structural biotechnology journal · 2025Review
- RNA fold prediction by Monte Carlo in graph space and the statistical mechanics of tertiary interactions.RNA (New York, N.Y.) · 2024Article
- Evaluating Performance of Different RNA Secondary Structure Prediction Programs Using Self-cleaving Ribozymes.Genomics, proteomics & bioinformatics · 2024Article
- Exploring the challenges of RNAi-based strategies for crop protection.Advanced biotechnology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Secondary structure plays an important role in determining the function of noncoding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine learning (ML) technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on ML technologies and a tabularized summary of the most important methods in this field. The current pending challenges in the field of RNA secondary structure prediction and future trends are also discussed.
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Registered trials
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.