Publication Details
Abstract
In genetic engineering, the revolutionary CRISPR-Cas system has emerged as a crucial tool for precise genome editing. At the same time, the emergence and rapid development of deep learning methods have provided new impetus for scientific research in genomic data analysis. Since advancements in both fields are occurring simultaneously, it is necessary to continuously monitor these rapidly evolving research directions. Significant progress has been achieved in utilizing deep learning to predict the activity of guide RNA (gRNA) within CRISPR-Cas systems. The activity of gRNA is a key factor determining the accuracy and efficiency of genome editing. By analyzing recent studies, it becomes evident that there have been remarkable achievements and new directions in integrating CRISPR-Cas systems with deep learning. This integration contributes to an important interdisciplinary field that bridges artificial intelligence and genetic engineering