ReviewBriefings in bioinformatics2023
Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: a review.
Review in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 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
49 citing papers in PubMed, 1 synthesis or guideline pooled it, 84 citations in OpenAlex.
- Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR.Journal of translational medicine · 2025Pooled it
- Minimizing Off-Target Effects of CRISPR-Cas9 With Optimized sgRNA: Evaluation of Efficiency and Specificity in the Tumor Protein 53 (TP53) Region.Biotechnology and bioengineering · 2026Article
- Beyond Earth: Recent Advancements in Microgravity Biomedical and Genetic Research in Saudi Arabia.International journal of molecular sciences · 2026Review
- Advancements in CRISPR/Cas Technologies for Sensitive Cancer Detection: Mechanisms, Platforms, and Clinical Translation Roadmap.Diagnostics (Basel, Switzerland) · 2026Review
- AI-Guided DNA-Free and Genotype-Independent Genome Editing for Soybean Improvement.Plants (Basel, Switzerland) · 2026Review
- Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.Biotechnology journal · 2026Review
- Expanding the Microbial Genomic Landscape and Biotechnological Applications of CRISPR-Cas Systems.Biology · 2026Review
- CRISPR-MBTF: a multi-branch transformer fusion framework for CRISPR-Cas9 off-target prediction.Briefings in bioinformatics · 2026Article
- 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
- 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
- PCdb: A comprehensive plant genome-editing database integrating sgRNA efficiency, off-target predictions, and epigenomic landscapes.Plant communications · 2026Article
- Building and Validating an Explainable Machine Learning Model for Predicting Health-Promoting Behaviors in Older Adults: A Multicenter Study.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- Harnessing artificial intelligence to advance CRISPR-based genome editing technologies.Nature reviews. Genetics · 2026Review
- CRISPR-based diagnostics for infectious diseases: mechanisms, advancements and clinical transformation prospects.Frontiers in cellular and infection microbiology · 2026Review
- CRISPR-HAWK: Haplotype- and Variant-aware guide design toolkit for CRISPR-Cas.bioRxiv : the preprint server for biology · 2025Article
- Precision gene editing: The power of CRISPR-Cas in modern genetics.Molecular therapy. Nucleic acids · 2025Review
- NDR2 Kinase Regulates Microglial Metabolic Adaptation and Inflammatory Response: Critical Role in Glucose-Dependent Functional Plasticity.International journal of molecular sciences · 2025Article
- Review
- Similarity-based transfer learning with deep learning networks for accurate CRISPR-Cas9 off-target prediction.PLoS computational biology · 2025Article
- Machine-Learning-Aided Advanced Electrochemical Biosensors.Advanced materials (Deerfield Beach, Fla.) · 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
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
CRISPR/Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR-associated protein 9) is a popular and effective two-component technology used for targeted genetic manipulation. It is currently the most versatile and accurate method of gene and genome editing, which benefits from a large variety of practical applications. For example, in biomedicine, it has been used in research related to cancer, virus infections, pathogen detection, and genetic diseases. Current CRISPR/Cas9 research is based on data-driven models for on- and off-target prediction as a cleavage may occur at non-target sequence locations. Nowadays, conventional machine learning and deep learning methods are applied on a regular basis to accurately predict on-target knockout efficacy and off-target profile of given single-guide RNAs (sgRNAs). In this paper, we present an overview and a comparative analysis of traditional machine learning and deep learning models used in CRISPR/Cas9. We highlight the key research challenges and directions associated with target activity prediction. We discuss recent advances in the sgRNA-DNA sequence encoding used in state-of-the-art on- and off-target prediction models. Furthermore, we present the most popular deep learning neural network architectures used in CRISPR/Cas9 prediction models. Finally, we summarize the existing challenges and discuss possible future investigations in the field of on- and off-target prediction. Our paper provides valuable support for academic and industrial researchers interested in the application of machine learning methods in the field of CRISPR/Cas9 genome editing.
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.