ArticleBMC genomics2025
UTR-Insight: integrating deep learning for efficient 5' UTR discovery and design.
Article in BMC genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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.
Who cites it
6 citing papers in PubMed.
- VaxLab: integrated platform for rapid multistrategy mRNA vaccine design.Experimental & molecular medicine · 2026Article
- UTR-DynaPro: a CNN-transformer multimodal language model for decoding 5'UTR regulatory mechanisms.Scientific reports · 2026Article
- mRNA therapeutics: Transforming medicine through innovation in design, delivery, and disease treatment.Molecular therapy. Nucleic acids · 2025Review
- Engineering Anti-Tumor Immunity: An Immunological Framework for mRNA Cancer Vaccines.Vaccines · 2025Review
- Deciphering the Contribution of TATA Box and 5'UTR to Defense Signaling in Rice Under Blast Infection.Biology · 2025Review
- GTAT-GRN: a graph topology-aware attention method with multi-source feature fusion for gene regulatory network inference.Frontiers in genetics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The 5' UTR is critical for mRNA stability and translation efficiency in therapeutics. We developed UTR-Insight, a model integrating a pretrained language model with a CNN-Transformer architecture, explaining 89.1% of the mean ribosome load (MRL) variation in random 5' UTRs and 82.8% in endogenous 5' UTRs, surpassing existing models. Using UTR-Insight, we performed high-throughput in silico screening of hundreds of thousands of endogenous 5' UTRs from primates, mice, and viruses. The screened sequences increased protein expression by up to 319% compared to the human α-globin 5' UTR, and UTR-Insight-designed sequences achieved even greater expression levels than high-performing endogenous 5' UTRs.
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What OpenQuestion holds
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.