ArticleBriefings in bioinformatics2023
RNAdegformer: accurate prediction of mRNA degradation at nucleotide resolution with deep learning.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- ContiTE: continuous manifold MoE for few-shot cross-tissue mRNA translation efficiency prediction.Briefings in bioinformatics · 2026Article
- Accelerate the Highly Efficient Development of mRNA Vaccines Through Advanced Computational Methods.MedComm · 2026Review
- RSV vaccine development: advances and fusion protein-focused strategies.Frontiers in immunology · 2026Review
- Assessment of transformer-based AI in clinical oncology.BMC medical informatics and decision making · 2025Article
- Enhancing mRNA translation efficiency with discriminative and generative artificial intelligence by optimizing 5' UTR sequences.iScience · 2025Article
- Decoding the interactions and functions of non-coding RNA with artificial intelligence.Nature reviews. Molecular cell biology · 2025Review
- Genomic medicine in hepatology: mechanisms and liver treatment strategies.Molecular medicine (Cambridge, Mass.) · 2025Review
- RegRNA 3.0: expanding regulatory RNA analysis with new features for motif, interaction, and annotation.Nucleic acids research · 2025Article
- Considerations for mRNA Product Development, Regulation and Deployment Across the Lifecycle.Vaccines · 2025Review
- Self-Amplifying RNA: Advantages and Challenges of a Versatile Platform for Vaccine Development.Viruses · 2025Review
- UTR-Insight: integrating deep learning for efficient 5' UTR discovery and design.BMC genomics · 2025Article
- Review
- mRNA Fragmentation Pattern Detected by SHAPE.Current issues in molecular biology · 2024Article
- Review
- Ribonanza: deep learning of RNA structure through dual crowdsourcing.bioRxiv : the preprint server for biology · 2024Article
- Personalized cancer vaccine design using AI-powered technologies.Frontiers in immunology · 2024Review
- Nucleic Transformer: Classifying DNA Sequences with Self-Attention and Convolutions.ACS synthetic biology · 2023Article
- Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review.Biology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Messenger RNA-based therapeutics have shown tremendous potential, as demonstrated by the rapid development of messenger RNA based vaccines for COVID-19. Nevertheless, distribution of mRNA vaccines worldwide has been hampered by mRNA's inherent thermal instability due to in-line hydrolysis, a chemical degradation reaction. Therefore, predicting and understanding RNA degradation is a crucial and urgent task. Here we present RNAdegformer, an effective and interpretable model architecture that excels in predicting RNA degradation. RNAdegformer processes RNA sequences with self-attention and convolutions, two deep learning techniques that have proved dominant in the fields of computer vision and natural language processing, while utilizing biophysical features of RNA. We demonstrate that RNAdegformer outperforms previous best methods at predicting degradation properties at nucleotide resolution for COVID-19 mRNA vaccines. RNAdegformer predictions also exhibit improved correlation with RNA in vitro half-life compared with previous best methods. Additionally, we showcase how direct visualization of self-attention maps assists informed decision-making. Further, our model reveals important features in determining mRNA degradation rates via leave-one-feature-out analysis.
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What Socratic holds
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 Socratic graph.