ArticleGenome biology2024
Current limitations in predicting mRNA translation with deep learning models.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Probing the limits of genetic recoding using multi-omics-guided evolution.Nature communications · 2026Article
- Predicting the translation efficiency of messenger RNA in mammalian cells.Nature biotechnology · 2026Article
- LUADnet: a deep learning model for prediction of clinical outcomes in lung adenocarcinoma based on gene expression signatures.Translational lung cancer research · 2026Article
- UTR-DynaPro: a CNN-transformer multimodal language model for decoding 5'UTR regulatory mechanisms.Scientific reports · 2026Article
- An overview of CRISPR-artificial intelligence theranostics: Current and emerging applications.Biomaterials translational · 2026Review
- GC3 codons enhance protein production in diverse GC- and AT-rich plant species.bioRxiv : the preprint server for biology · 2025Article
- HydraRNA: a hybrid architecture based full-length RNA language model.Genome biology · 2025Article
- Decoding the interactions and functions of non-coding RNA with artificial intelligence.Nature reviews. Molecular cell biology · 2025Review
- Machine learning-based analysis of the impact of 5' untranslated region on protein expression.Nucleic acids research · 2025Article
- Multi-omic assessment of mRNA translation dynamics in liver cancer cell lines.Scientific data · 2025Article
- A generalized and efficient approach for complete mRNA design improves translation, stability and specificity.bioRxiv : the preprint server for biology · 2025Article
- UTR-Insight: integrating deep learning for efficient 5' UTR discovery and design.BMC genomics · 2025Article
- Improving the generalization of protein expression models with mechanistic sequence information.Nucleic acids research · 2025Article
- Interpreting deep neural networks for the prediction of translation rates.BMC genomics · 2024Article
- Current limitations in predicting mRNA translation with deep learning models.Genome biology · 2024Article
- Synthetic genomes unveil the effects of synonymous recoding.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundThe design of nucleotide sequences with defined properties is a long-standing problem in bioengineering. An important application is protein expression, be it in the context of research or the production of mRNA vaccines. The rate of protein synthesis depends on the 5' untranslated region (5'UTR) of the mRNAs, and recently, deep learning models were proposed to predict the translation output of mRNAs from the 5'UTR sequence. At the same time, large data sets of endogenous and reporter mRNA translation have become available.
resultsIn this study, we use complementary data obtained in two different cell types to assess the accuracy and generality of currently available models for predicting translational output. We find that while performing well on the data sets on which they were trained, deep learning models do not generalize well to other data sets, in particular of endogenous mRNAs, which differ in many properties from reporter constructs.
conclusionsThese differences limit the ability of deep learning models to uncover mechanisms of translation control and to predict the impact of genetic variation. We suggest directions that combine high-throughput measurements and machine learning to unravel mechanisms of translation control and improve construct design.
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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 Socratic graph.