ArticleBioinformatics (Oxford, England)2026
Deciphering the comprehensive relationship between 5' UTR and 3' UTR sequences with deep learning.
Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- A generalized and efficient approach for complete mRNA design improves translation, stability and specificity.bioRxiv : the preprint server for biology · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
motivationRecent advances in mRNA therapeutics have driven further research on the untranslated regions (UTRs) of mRNA. However, prior studies have mainly focused on either the 5' or 3' UTR individually. Increasing evidence suggests potential cooperative effects between these two regions, which remain largely unexplored in computational studies.
resultsWe present a deep learning-based approach to predicting relationships between 5' and 3' UTRs by leveraging latent representations from a pre-trained RNA language model and contrastive learning. Our method effectively identifies highly related UTRs, uncovering sequence and expression characteristics that suggest functional interplay. Our analysis revealed that Highly Related UTRs (HRUs) are significantly enriched in genes associated with neural development, exhibit distinctive UTR length and secondary structure characteristics, and are involved in cell type-specific regulation of translation efficiency. These findings provide new insights into UTR co-optimization for mRNA therapeutics. AVAILABILITY: The source code is available for free at https://github.com/hmdlab/utr_pairpred.git. The data and intermediate files used in our analysis are available at https://waseda.box.com/v/utr-pairpred-data.
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