ArticleGenome research2024
CodonBERT large language model for mRNA vaccines.
Article in Genome research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- AI foundation models for RNA biology.RNA biology · 2026Review
- Towards mRNA therapeutics 2.0.Nature reviews. Drug discovery · 2026Review
- Cold-adapted RNA polymerase fromProceedings of the National Academy of Sciences of the United States of America · 2026Article
- Two Classes of Protein Therapeutics: Why Dose-Response Architecture Defines the Boundary of mRNA Medicines.Pharmaceutics · 2026Review
- seq2ribo: structure-aware integration of machine learning and simulation to predict ribosome location profiles from RNA sequences.Bioinformatics (Oxford, England) · 2026Article
- Deep Learning-Guided Reverse Translation Enhances Soluble Expression of Recombinant Proteins inInternational journal of molecular sciences · 2026Article
- Bridging technical innovation and computational advances in studies of RNA-protein assemblies.Nature reviews. Genetics · 2026Review
- Transfer learning with pre-trained language models for protein expression level prediction inSynthetic and systems biotechnology · 2026Article
- CodonMoE: DNA language models for codon-dependent mRNA prediction.Bioinformatics (Oxford, England) · 2026Article
- Predicting the translation efficiency of messenger RNA in mammalian cells.Nature biotechnology · 2026Article
- Decoding the language of messenger RNA.Nature methods · 2026Article
- seq2ribo: Structure-aware integration of machine learning and simulation to predict ribosome location profiles from RNA sequences.bioRxiv : the preprint server for biology · 2026Article
- VaxLab: integrated platform for rapid multistrategy mRNA vaccine design.Experimental & molecular medicine · 2026Article
- The DNA dialect: a comprehensive guide to pretrained genomic language models.Molecular systems biology · 2026Review
- Advancing codon language modeling with synonymous codon constrained masking.Nucleic acids research · 2026Article
- A comprehensive survey of genome language models in bioinformatics.Briefings in bioinformatics · 2026Review
- Recent Progress in Sequence Optimization of mRNA Vaccine: Biological Mechanism, Quantitative Metrics, and Computational Model.Bioinformatics and biology insights · 2026Review
- Protein Language Models in Virology: A Review of Advances and Applications.Methods in molecular biology (Clifton, N.J.) · 2026Review
- AI-powered mapping of tumor immunity for optimized mRNA vaccine engineering.Frontiers in oncology · 2026Review
- mRNA therapeutics: Transforming medicine through innovation in design, delivery, and disease treatment.Molecular therapy. Nucleic acids · 2025Review
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Authors and funding
27 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
mRNA-based vaccines and therapeutics are gaining popularity and usage across a wide range of conditions. One of the critical issues when designing such mRNAs is sequence optimization. Even small proteins or peptides can be encoded by an enormously large number of mRNAs. The actual mRNA sequence can have a large impact on several properties, including expression, stability, immunogenicity, and more. To enable the selection of an optimal sequence, we developed CodonBERT, a large language model (LLM) for mRNAs. Unlike prior models, CodonBERT uses codons as inputs, which enables it to learn better representations. CodonBERT was trained using more than 10 million mRNA sequences from a diverse set of organisms. The resulting model captures important biological concepts. CodonBERT can also be extended to perform prediction tasks for various mRNA properties. CodonBERT outperforms previous mRNA prediction methods, including on a new flu vaccine data set.
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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.