Precision gene editing using deep learning: A case study of the CRISPR-Cas9 editor

Abstract

This article reviews the application cases of CRISPR/Cas9 gene editing technology, as well as the challenges and limitations. Firstly, the application of CRISPR/Cas9 technology based on deep learning in predicting the targeting efficiency of sgRNA is introduced, and the steps of data acquisition, pre-processing and feature engineering are described in detail. It then discusses the non-specific cutting and cytotoxicity challenges of CRISPR/Cas9 technology, as well as strategies for solving these challenges using deep learning techniques. Finally, the paper emphasizes the importance of deep learning techniques to mitigate the cytotoxicity problems in CRISPR/Cas9 technology, and points out that the establishment of these models can improve the safety and efficiency of gene editing experiments, and provide important reference and guidance for research in related fields.