ArticleMolecular therapy. Nucleic acids2024
DTMP-prime: A deep transformer-based model for predicting prime editing efficiency and PegRNA activity.
Article in Molecular therapy. Nucleic acids, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled 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.
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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Advances in prime editing: Molecular innovations, Large-fragment engineering, and AI-driven design.Biodesign research · 2026Review
- Transgene-free genome editing in plants.aBIOTECH · 2026Review
- Prime editing: Emerging mechanisms, engineering innovations, and next-generation applications.Biodesign research · 2026Review
- Prospects and challenges in using engineered lactic acid bacteria in aquaculture applications.Engineering microbiology · 2026Review
- CRISPR-MBTF: a multi-branch transformer fusion framework for CRISPR-Cas9 off-target prediction.Briefings in bioinformatics · 2026Article
- Harnessing artificial intelligence to advance CRISPR-based genome editing technologies.Nature reviews. Genetics · 2026Review
- Advancing CRISPR with deep learning: A comprehensive review of models and databases.Molecular therapy. Nucleic acids · 2025Review
- Base and Prime Editing for Inherited Retinal Diseases: Delivery Platforms, Safety, Efficacy, and Translational Perspectives.Pharmaceutics · 2025Review
- From Code to Life: The AI-Driven Revolution in Genome Editing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prime editors are CRISPR-based genome engineering tools with significant potential for rectifying patient mutations. However, their usage requires experimental optimization of the prime editing guide RNA (PegRNA) to achieve high editing efficiency. This paper introduces the deep transformer-based model for predicting prime editing efficiency (DTMP-Prime), a tool specifically designed to predict PegRNA activity and prime editing (PE) efficiency. DTMP-Prime facilitates the design of appropriate PegRNA and ngRNA. A transformer-based model was constructed to scrutinize a wide-ranging set of PE data, enabling the extraction of effective features of PegRNAs and target DNA sequences. The integration of these features with the proposed encoding strategy and DNABERT-based embedding has notably improved the predictive capabilities of DTMP-Prime for off-target sites. Moreover, DTMP-Prime is a promising tool for precisely predicting off-target sites in CRISPR experiments. The integration of a multi-head attention framework has additionally improved the precision and generalizability of DTMP-Prime across various PE models and cell lines. Evaluation results based on the Pearson and Spearman correlation coefficient demonstrate that DTMP-Prime outperforms other state-of-the-art models in predicting the efficiency and outcomes of PE experiments.
Indexed as
Identifiers
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.