ArticleNature machine intelligence2022
Deep learning models for predicting RNA degradation via dual crowdsourcing.
Article in Nature machine intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- From RNA design to delivery: Computational strategies for functional RNA therapeutics.Bioactive materials · 2027Review
- mRNA lipid nanoparticle vaccines: current status, challenges and future prospects.Molecular biomedicine · 2026Review
- Gradient-based optimization for mRNA sequence design.Bioinformatics (Oxford, England) · 2026Article
- Next-Generation Nanocarrier Platforms for RNA Vaccines: Advances in Formulation, Stability Engineering, and Translational Manufacturing Challenges.Pharmaceutics · 2026Review
- Crowdsourced riboregulators reveal design principles for programmable RNA switching.bioRxiv : the preprint server for biology · 2026Article
- Analytical Characterization and Stability Assessment of RNA-Based Vaccines.Pharmaceutics · 2026Review
- Fine-scale structural information substantially improves mRNA therapeutic stability prediction.Molecular therapy. Nucleic acids · 2026Article
- Advances in Protein Function Prediction from the Fifth CAFA Challenge.bioRxiv : the preprint server for biology · 2026Article
- Social Justice, Science Agency, and Collaborations: Using Participatory Science to Enhance Learning Outcomes in Postsecondary STEM Courses.CBE life sciences education · 2026Article
- Accelerate the Highly Efficient Development of mRNA Vaccines Through Advanced Computational Methods.MedComm · 2026Review
- Engineering the poly(A) tail for therapeutic mRNA: from expression control to manufacturing robustness.Frontiers in bioengineering and biotechnology · 2026Review
- AI-powered mapping of tumor immunity for optimized mRNA vaccine engineering.Frontiers in oncology · 2026Review
- Template-based RNA structure prediction advanced through a blind code competition.bioRxiv : the preprint server for biology · 2025Article
- mRNABERT: advancing mRNA sequence design with a universal language model and comprehensive dataset.Nature communications · 2025Article
- Decoding the interactions and functions of non-coding RNA with artificial intelligence.Nature reviews. Molecular cell biology · 2025Review
- mRNABench: A curated benchmark for mature mRNA property and function prediction.bioRxiv : the preprint server for biology · 2025Article
- RegRNA 3.0: expanding regulatory RNA analysis with new features for motif, interaction, and annotation.Nucleic acids research · 2025Article
- POCALI: Prediction and Insight on CAncer LncRNAs by Integrating Multi-Omics Data with Machine Learning.Small methods · 2025Article
- SCIG: Machine learning uncovers cell identity genes in single cells by genetic sequence codes.Nucleic acids research · 2025Article
- Generative and predictive neural networks for the design of functional RNA molecules.Nature communications · 2025Article
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29 authors.
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Abstract
Medicines based on messenger RNA (mRNA) hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition ('Stanford OpenVaccine') on Kaggle, involving single-nucleotide resolution measurements on 6,043 diverse 102-130-nucleotide RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504-1,588 nucleotides) with improved accuracy compared with previously published models. These results indicate that such models can represent in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for dataset creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales.
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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.