Computational Molecular Biology 2025, Vol.15, No.2, 102-111 http://bioscipublisher.com/index.php/cmb 10 5 Figure 1 Overview of deep learning for predicting CRISPR/Cas9 sgRNA on-target activity that involved the following steps: (i) data collection and preprocessing; (ii) data representation; (iii) deep learning models. Indirect features extracted from sgRNA sequences could be combined as input for deep learning. (iv) Performance evaluation (Adopted from Toufikuzzaman et al., 2024) 5 CRISPR Off-Target Effect Prediction Model Based on Deep Learning 5.1 DeepCRISPR deep learning framework DeepCRISPR is one of the earliest models to apply deep learning methods for CRISPR off-target prediction. It uses convolutional neural networks (CNNS) to automatically extract sequence features, no longer relying on traditional manually set metrics. By inputting target sequences and potential off-target sequences, the model can learn the complex relationships among mismatch distribution, PAM environment and sequence context, thereby improving the accuracy and generalization ability of prediction (Chuai et al., 2018). The advantage of DeepCRISPR lies in its ability to handle large-scale data and achieve automated prediction through an end-to-end approach. Research shows that this model significantly outperforms traditional models such as MIT and CFD in terms of ROC curve and AUC value. However, DeepCRISPR is highly dependent on training data. If the dataset is biased towards certain genomes or species, it may lead to insufficient universality of prediction.
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