Evidence map›Paper›PMID 38855263›Full record

ArticleNature machine intelligence2024

A 5' UTR Language Model for Decoding Untranslated Regions of mRNA and Function Predictions.

Yanyi Chu, Dan Yu, Yupeng Li, Kaixuan Huang, Yue Shen, Le Cong, Jason Zhang, Mengdi Wang

Abstract read
In one paragraph

Article in Nature machine intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.

0numbers the graph read from it
0cells of the map it votes in
69citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

69 citing papers in PubMed.

  1. Functional analysis ofMolecular therapy. Nucleic acids · 2026
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  9. Article
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  11. Enhancement of Therapeutic mRNA Translation in Cellular Stress Conditions.International journal of molecular sciences · 2026
    Review
  12. Article
  13. Article
  14. Article
  15. Review
  16. "More" Artificial mRNAs: Beyond the Art of Nature.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  17. Article
  18. Article
  19. Article
  20. Review

9 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Yanyi ChuCenter for Statistics and Machine Learning and Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA.
Dan YuRVAC Medicines, Waltham, MA 02451, USA.
Yupeng LiRVAC Medicines, Waltham, MA 02451, USA.
Kaixuan HuangCenter for Statistics and Machine Learning and Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA.
Yue ShenRVAC Medicines, Waltham, MA 02451, USA.
Le CongDepartment of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.
Jason ZhangZipcode Bio, Weston, MA 02493, USA.
Mengdi WangCenter for Statistics and Machine Learning and Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA.

Funding

Towards Robust Multiplex Genome Engineering Beyond CRISPR-Cas9R35HG011316 · NHGRI · STANFORD UNIVERSITY · PI CONG, LE · 2020 to 2025
$2.8M
NHGRI NIH HHS R35 HG011316
6 · The paper itself

Abstract

The 5' UTR, a regulatory region at the beginning of an mRNA molecule, plays a crucial role in regulating the translation process and impacts the protein expression level. Language models have showcased their effectiveness in decoding the functions of protein and genome sequences. Here, we introduced a language model for 5' UTR, which we refer to as the UTR-LM. The UTR-LM is pre-trained on endogenous 5' UTRs from multiple species and is further augmented with supervised information including secondary structure and minimum free energy. We fine-tuned the UTR-LM in a variety of downstream tasks. The model outperformed the best known benchmark by up to 5% for predicting the Mean Ribosome Loading, and by up to 8% for predicting the Translation Efficiency and the mRNA Expression Level. The model also applies to identifying unannotated Internal Ribosome Entry Sites within the untranslated region and improves the AUPR from 0.37 to 0.52 compared to the best baseline. Further, we designed a library of 211 novel 5' UTRs with high predicted values of translation efficiency and evaluated them via a wet-lab assay. Experiment results confirmed that our top designs achieved a 32.5% increase in protein production level relative to well-established 5' UTR optimized for therapeutics.

Indexed as

5’ untranslated regiongene expressioninternal ribosome entry sitelanguage modelmRNAribosome loadingself-supervised learningtranslation efficiency

Identifiers

PMID38855263
PMCPMC11155392

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Registered trials

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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.