[Application of machine learning in the CRISPR/Cas9 system].

Area of Science:

• Molecular Biology
• Bioinformatics

Background:

• CRISPR/Cas9 technology is crucial for gene editing and regulation.
• Improving CRISPR/Cas9 on-target efficiency and specificity while minimizing off-target effects remains a challenge.

Purpose of the Study:

• To review the mechanism of CRISPR/Cas9.
• To discuss current limitations including efficiency, off-target effects, and PAM recognition.
• To summarize machine learning applications for optimizing CRISPR/Cas9.

Main Methods:

• Review of CRISPR/Cas9 mechanism and limitations.
• Exploration of machine learning frameworks for CRISPR/Cas9 optimization.
• Summary of applications including sgRNA design, efficiency prediction, and off-target evaluation.

Main Results:

• Machine learning offers novel solutions to CRISPR/Cas9 system challenges.
• Applications include optimized sgRNA design and accurate prediction of cleavage efficiency.
• Machine learning aids in evaluating off-target effects and facilitating genetic screening.

Conclusions:

• Machine learning is a powerful tool for advancing CRISPR/Cas9 technology.
• Optimizing CRISPR/Cas9 through machine learning enhances its utility in gene editing and functional genomics.
• Future prospects involve further integration of AI for more precise and efficient genome engineering.