ArticleInternational journal of molecular sciences2024
Crispr-SGRU: Prediction of CRISPR/Cas9 Off-Target Activities with Mismatches and Indels Using Stacked BiGRU.
Article in International journal of molecular sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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
6 citing papers in PubMed.
- 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
- Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.Biotechnology journal · 2026Review
- A review of flavonoids at the crossroads of plant defense: integrating biotic and abiotic stress tolerance through AI- and CRISPR/Cas-guided metabolic reprogramming.Frontiers in plant science · 2026Review
- From Code to Life: The AI-Driven Revolution in Genome Editing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Deep Learning Based Models for CRISPR/Cas Off-Target Prediction.Small methods · 2025Review
- AI-driven advances in plant biotechnology: sharpening the edge of plant tissue culture and genome editing.Frontiers in plant science · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
CRISPR/Cas9 is a popular genome editing technology, yet its clinical application is hindered by off-target effects. Many deep learning-based methods are available for off-target prediction. However, few can predict off-target activities with insertions or deletions (indels) between single guide RNA and DNA sequence pairs. Additionally, the analysis of off-target data is challenged due to a data imbalance issue. Moreover, the prediction accuracy and interpretability remain to be improved. Here, we introduce a deep learning-based framework, named Crispr-SGRU, to predict off-target activities with mismatches and indels. This model is based on Inception and stacked BiGRU. It adopts a dice loss function to solve the inherent imbalance issue. Experimental results show our model outperforms existing methods for off-target prediction in terms of accuracy and robustness. Finally, we study the interpretability of this model through Deep SHAP and teacher-student-based knowledge distillation, and find it can provide meaningful explanations for sequence patterns regarding off-target activity.
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Registered trials
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