Key result
Deep learning models demonstrate high accuracy and interpretability in predicting CRISPR-Cas9 off-target events.
Why the study?
CRISPR-Cas9 off-target effects limit safety and efficacy, and existing machine learning prediction models still face issues regarding accuracy and interpretability.
Deep learning models show promise in improving the accuracy and interpretability of predicting CRISPR-Cas9 off-target effects, which could enhance the safety of gene editing.
CRISPI-Cas 9 technology is currently the most popular gene editing tool, which has been used in biomedical research and clinical treatment, but it also has the problem of off-target effects that cause unexpected gene mutations, which limits its safety and efficacy. Now, various machine learning models have been trained to predict off-target events in order to: Gene editing is more precise. In terms of both accuracy and interpretability there are still issues in the existing models. This paper has focused on reviewing Machine learning archery Off Target Prediction models and their performance. The results of this study show that the deep learning model has high accuracy and interpretability in the prediction of off-target events, which provides a reference for the optimization of gene editing strategies. The results reveal that there is a lot of room for development of deep learning technology in improving the safety of gene editing, which provides an in-depth reference for the sustainable development of gene editing technology. The application of this study to this paper also has its limitations, including the inadequacy of the applicability of the model in multiple gene editing contexts. In the future research of gene editing, more attention can be paid to the study of new biological indicators, the establishment of more accurate prediction models, and the improvement of models with more explanatory interpretation, so as to more safely apply gene editing technology in clinical and agricultural treatment and improvement.
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Jingbo Wang (2025) conducted a review in CRISPR-Cas9 off-target effects. Machine learning and deep learning models vs. Baseline models or traditional methods was evaluated on Prediction accuracy of off-target events. Deep learning models demonstrate high accuracy and interpretability in predicting CRISPR-Cas9 off-target events, offering significant potential to optimize gene-editing strategies and improve safety.
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