ReviewMolecular therapy. Nucleic acids2025
Advancing CRISPR with deep learning: A comprehensive review of models and databases.
Review in Molecular therapy. Nucleic acids, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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
8 citing papers in PubMed.
- Advancements in CRISPR/Cas Technologies for Sensitive Cancer Detection: Mechanisms, Platforms, and Clinical Translation Roadmap.Diagnostics (Basel, Switzerland) · 2026Review
- K-attention: a biologically informed attention operator for data-efficient sequence-based omics modeling.Briefings in bioinformatics · 2026Article
- Overcoming the challenges of genome-editing essential genes.STAR protocols · 2026Review
- 10 years of CRISPR/CAS genomic engineering in Yarrowia lipolytica.Bioprocess and biosystems engineering · 2026Review
- Machine Learning for CRISPR-Based Diagnostics.International journal of molecular sciences · 2026Review
- Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.Biotechnology journal · 2026Review
- Deep learning-driven prediction of on-target activity, off-target risk, and repair outcomes in CRISPR/Cas9: current landscape and multi-scale perspectives.Journal of translational medicine · 2026Review
- Review
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
CRISPR is considered a powerful tool for targeted genome editing. However, off-target effects remain a significant challenge in the CRISPR field, hindering its broader clinical application. To enhance the development of gene-editing therapies, it is essential to predict the efficiency of CRISPR-based genome editing experiments, before trying them on clinical cases. Machine learning (ML) and deep learning (DL) tools are projected to become the leading methods for predicting CRISPR on-target and off-target activity. Current prediction accuracy is limited by the amount of available training data. As more sequence features are identified and incorporated in DL tools, predictions of them are expected to better align with experimental results. Hence, the increasing focus on ML/DL approaches to predict off-target sites necessitates large and easily searchable databases. In this review, we will take a closer look at available CRISPR databases.
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.