Li Chuxuan, Zou Quan, Li Jian, Feng Hailin
School of Mathematics and Computer science, Zhejiang A&F University, Hangzhou 311300, China.
Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China.
Comput Struct Biotechnol J. 2025 May 27;27:2098-2106. doi: 10.1016/j.csbj.2025.05.001. eCollection 2025.
CRISPR-Cas9 is a groundbreaking gene editing technology, but variations in targeted editing efficiency arise due to significant discrepancies in sgRNA activity. Therefore, improving the prediction accuracy of sgRNA activity is crucial for its safety and effectiveness. Deep learning methods have surpassed traditional scoring and machine learning methods, demonstrating higher prediction accuracy and scalability. However, challenges persist in local feature extraction, cross-sequence dependency modeling, and dynamic feature weight assignment. To address these issues, we introduce CRISPR_HNN, a hybrid deep neural network model that integrates MSC, MHSA, and BiGRU to effectively capture local dynamic features and global long-distance dependencies. In addition, it adopts One-hot Encoding and Label Encoding strategies. Experimental results demonstrate that CRISPR_HNN surpasses existing models on public datasets and substantially enhances the accuracy of sgRNA activity prediction.
CRISPR-Cas9是一种开创性的基因编辑技术，但由于sgRNA活性存在显著差异，导致靶向编辑效率出现变化。因此，提高sgRNA活性的预测准确性对其安全性和有效性至关重要。深度学习方法已超越传统评分和机器学习方法，展现出更高的预测准确性和可扩展性。然而，在局部特征提取、跨序列依赖性建模和动态特征权重分配方面仍存在挑战。为解决这些问题，我们引入了CRISPR_HNN，这是一种混合深度神经网络模型，它集成了MSC、MHSA和BiGRU，以有效捕获局部动态特征和全局长距离依赖性。此外，它采用了独热编码和标签编码策略。实验结果表明，CRISPR_HNN在公共数据集上超越了现有模型，并大幅提高了sgRNA活性预测的准确性。
