ArticleComputational and structural biotechnology journal2021
Prediction of CRISPR/Cas9 single guide RNA cleavage efficiency and specificity by attention-based convolutional neural networks.
Article in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR.Journal of translational medicine · 2025Pooled it
- 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
- CrisprPr: a hybrid-driven framework for CRISPR/Cas9 off-target prediction with analysis of prior-information updates.Briefings in bioinformatics · 2026Article
- Review
- Similarity-based transfer learning with deep learning networks for accurate CRISPR-Cas9 off-target prediction.PLoS computational biology · 2025Article
- CRISPR-GATE: a one-stop repository and guide to computational resources for genome editing experimentation.Briefings in bioinformatics · 2025Review
- Graph-CRISPR: a gene editing efficiency prediction model based on graph neural network with integrated sequence and secondary structure feature extraction.Briefings in bioinformatics · 2025Article
- Leveraging protein language models for cross-variant CRISPR/Cas9 sgRNA activity prediction.Bioinformatics (Oxford, England) · 2025Article
- Deep Learning Based Models for CRISPR/Cas Off-Target Prediction.Small methods · 2025Review
- Review
- DeepMEns: an ensemble model for predicting sgRNA on-target activity based on multiple features.Briefings in functional genomics · 2025Article
- The Evolution of Nucleic Acid-Based Diagnosis Methods from the (pre-)CRISPR to CRISPR era and the Associated Machine/Deep Learning Approaches in Relevant RNA Design.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Unraveling the future of genomics: CRISPR, single-cell omics, and the applications in cancer and immunology.Frontiers in genome editing · 2025Review
- Synergizing CRISPR-Cas9 with Advanced Artificial Intelligence and Machine Learning for Precision Drug Delivery: Technological Nexus and Regulatory Insights.Current gene therapy · 2025Review
- AI-driven advances in plant biotechnology: sharpening the edge of plant tissue culture and genome editing.Frontiers in plant science · 2025Review
- Prediction of CRISPR-Cas9 on-target activity based on a hybrid neural network.Computational and structural biotechnology journal · 2025Article
- CRISPR-FMC: a dual-branch hybrid network for predicting CRISPR-Cas9 on-target activity.Frontiers in genome editing · 2025Article
- Overcoming CRISPR-Cas9 off-target prediction hurdles: A novel approach with ESB rebalancing strategy and CRISPR-MCA model.PLoS computational biology · 2024Article
- CrnnCrispr: An Interpretable Deep Learning Method for CRISPR/Cas9 sgRNA On-Target Activity Prediction.International journal of molecular sciences · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
CRISPR/Cas9 is a preferred genome editing tool and has been widely adapted to ranges of disciplines, from molecular biology to gene therapy. A key prerequisite for the success of CRISPR/Cas9 is its capacity to distinguish between single guide RNAs (sgRNAs) on target and homologous off-target sites. Thus, optimized design of sgRNAs by maximizing their on-target activity and minimizing their potential off-target mutations are crucial concerns for this system. Several deep learning models have been developed for comprehensive understanding of sgRNA cleavage efficacy and specificity. Although the proposed methods yield the performance results by automatically learning a suitable representation from the input data, there is still room for the improvement of accuracy and interpretability. Here, we propose novel interpretable attention-based convolutional neural networks, namely CRISPR-ONT and CRISPR-OFFT, for the prediction of CRISPR/Cas9 sgRNA on- and off-target activities, respectively. Experimental tests on public datasets demonstrate that our models significantly yield satisfactory results in terms of accuracy and interpretability. Our findings contribute to the understanding of how RNA-guide Cas9 nucleases scan the mammalian genome. Data and source codes are available at https://github.com/Peppags/CRISPRont-CRISPRofft.
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Registered trials
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