Evidence map›Paper›PMID 39898548›Full record

ArticleNucleic acids research2025

mRNA-LM: full-length integrated SLM for mRNA analysis.

Sizhen Li, Shahriar Noroozizadeh, Saeed Moayedpour, Lorenzo Kogler-Anele, Zexin Xue, Dinghai Zheng, Fernando Ulloa Montoya, Vikram Agarwal, Ziv Bar-Joseph, Sven Jager

Abstract read
In one paragraph

Article in Nucleic acids research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Review
  6. Protein Language Models in Virology: A Review of Advances and Applications.Methods in molecular biology (Clifton, N.J.) · 2026
    Review
  7. Review
  8. Orthrus: Towards Evolutionary and Functional RNA Foundation Models.bioRxiv : the preprint server for biology · 2025
    Article
  9. Article
  10. Review
  11. Article
  12. Review
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

10 authors.

Sizhen LiDigital R&D, Sanofi, Cambridge, MA 02141, United States.ORCID 0000-0001-7807-5748
Shahriar NoroozizadehDigital R&D, Sanofi, Cambridge, MA 02141, United States.
Saeed MoayedpourDigital R&D, Sanofi, Cambridge, MA 02141, United States.
Lorenzo Kogler-AneleDigital R&D, Sanofi, Cambridge, MA 02141, United States.
Zexin XueDigital R&D, Sanofi, Cambridge, MA 02141, United States.
Dinghai ZhengmRNA Center of Excellence, Sanofi, Waltham, MA 02451, United States.
Fernando Ulloa MontoyamRNA Center of Excellence, Sanofi, Marcy L'Etoile 69280, France.
Vikram AgarwalmRNA Center of Excellence, Sanofi, Waltham, MA 02451, United States.
Ziv Bar-JosephDigital R&D, Sanofi, Cambridge, MA 02141, United States.ORCID 0000-0003-3430-6051
Sven JagerDigital R&D, Sanofi, Cambridge, MA 02141, United States.

Funding

Sanofi
6 · The paper itself

Abstract

The success of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) messenger RNA (mRNA) vaccine has led to increased interest in the design and use of mRNA for vaccines and therapeutics. Still, selecting the most appropriate mRNA sequence for a protein remains a challenge. Several recent studies have shown that the specific mRNA sequence can have a significant impact on the translation efficiency, half-life, degradation rates, and other issues that play a major role in determining vaccine efficiency. To enable the selection of the most appropriate sequence, we developed mRNA-LM, an integrated small language model for modeling the entire mRNA sequence. mRNA-LM uses the contrastive language-image pretraining integration technology to combine three separate language models for the different mRNA segments. We trained mRNA-LM on millions of diverse mRNA sequences from several different species. The unsupervised model was able to learn meaningful biology related to evolution and host-pathogen interactions. Fine-tuning of mRNA-LM allowed us to use it in several mRNA property prediction tasks. As we show, using the full-length integrated model led to accurate predictions, improving on prior methods proposed for this task.

Indexed as

RNA, MessengerRNA, ViralSARS-CoV-2Sequence Analysis, RNACOVID-19COVID-19 VaccinesHost-Pathogen InteractionsHumansCOVID-19 VaccinesRNA, MessengerRNA, Viral

Identifiers

PMID39898548
PMCPMC11962594

What OpenQuestion holds

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Registered trials

None linked

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.