ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024
Deciphering 3'UTR Mediated Gene Regulation Using Interpretable Deep Representation Learning.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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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.
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Who cites it
18 citing papers in PubMed.
- AI foundation models for RNA biology.RNA biology · 2026Review
- Two Classes of Protein Therapeutics: Why Dose-Response Architecture Defines the Boundary of mRNA Medicines.Pharmaceutics · 2026Review
- Advancing bioinformatics with language models: components, applications, and perspectives.Briefings in bioinformatics · 2026Review
- Bridging sequence-structure motifs and genetic variants for genome-wide dynamic RNA-protein interaction profiling.Nature communications · 2026Article
- The DNA dialect: a comprehensive guide to pretrained genomic language models.Molecular systems biology · 2026Review
- Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning.Briefings in bioinformatics · 2026Article
- Accelerate the Highly Efficient Development of mRNA Vaccines Through Advanced Computational Methods.MedComm · 2026Review
- A comprehensive survey of genome language models in bioinformatics.Briefings in bioinformatics · 2026Review
- mRNA therapeutics: Transforming medicine through innovation in design, delivery, and disease treatment.Molecular therapy. Nucleic acids · 2025Review
- mRNABERT: advancing mRNA sequence design with a universal language model and comprehensive dataset.Nature communications · 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
- MVRBind: multi-view learning for RNA-small molecule binding site prediction.Briefings in bioinformatics · 2025Article
- mRNABench: A curated benchmark for mature mRNA property and function prediction.bioRxiv : the preprint server for biology · 2025Article
- mRNA-LM: full-length integrated SLM for mRNA analysis.Nucleic acids research · 2025Article
- A generative framework for enhanced cell-type specificity in rationally designed mRNAs.bioRxiv : the preprint server for biology · 2024Article
- EnrichRBP: an automated and interpretable computational platform for predicting and analysing RNA-binding protein events.Bioinformatics (Oxford, England) · 2024Article
- Deciphering 3'UTR Mediated Gene Regulation Using Interpretable Deep Representation Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The 3' untranslated regions (3'UTRs) of messenger RNAs contain many important cis-regulatory elements that are under functional and evolutionary constraints. It is hypothesized that these constraints are similar to grammars and syntaxes in human languages and can be modeled by advanced natural language techniques such as Transformers, which has been very effective in modeling complex protein sequence and structures. Here 3UTRBERT is described, which implements an attention-based language model, i.e., Bidirectional Encoder Representations from Transformers (BERT). 3UTRBERT is pre-trained on aggregated 3'UTR sequences of human mRNAs in a task-agnostic manner; the pre-trained model is then fine-tuned for specific downstream tasks such as identifying RBP binding sites, m6A RNA modification sites, and predicting RNA sub-cellular localizations. Benchmark results show that 3UTRBERT generally outperformed other contemporary methods in each of these tasks. More importantly, the self-attention mechanism within 3UTRBERT allows direct visualization of the semantic relationship between sequence elements and effectively identifies regions with important regulatory potential. It is expected that 3UTRBERT model can serve as the foundational tool to analyze various sequence labeling tasks within the 3'UTR fields, thus enhancing the decipherability of post-transcriptional regulatory mechanisms.
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What Socratic 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 Socratic graph.