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Artificial intelligence (AI) plays a critical role in predicting and improving genome editing methods, including CRISPR/Cas9. Recently, several AI models, such as DeepCRISPR, CRISTA, and Deep High Fidelity (DeepHF), have been utilized to design guide RNAs (gRNAs) for CRISPR-Cas systems. These models assess genomic context, desired mutation type, on-target and off-target scores, and potential off-target locations. AI models help improve various genome editing methods, such as base, prime, and epigenome editing, which allow for precise and intentional changes to DNA sequences without the need for donor DNA templates. Furthermore, integrating AI with genome editing and precision medicine enables the creation of personalized treatments tailored to each individual’s unique genetic profile. Examining genomic data enables the identification of mutations, variations, and biomarkers linked to diseases like cancer, diabetes, and Alzheimer’s disease. Integrating AI with genome editing can potentially enhance genetic modification techniques’ precision, efficiency, and cost-effectiveness. Furthermore, it presents novel prospects for fields like genetics, biomedicine, and healthcare, which could significantly impact human health. However, several challenges still exist, including high costs, inaccurate edits, and effective delivery methods for CRISPR components, improved editing performance, and safety in clinical applications.

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Artificial Intelligence-Based Genome Editing in CRISPR/Cas9

  • 实验方案

  • 发表日期：

    2025年

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  • Shivangi Pandey,
  • Jyoti Kant Choudhari,
  • Abhishek Tripathi,
  • Akanksha Singh,
  • Anna Antony,
  • Usha Chouhan

摘要

Artificial intelligence (AI) plays a critical role in predicting and improving genome editing methods, including CRISPR/Cas9. Recently, several AI models, such as DeepCRISPR, CRISTA, and Deep High Fidelity (DeepHF), have been utilized to design guide RNAs (gRNAs) for CRISPR-Cas systems. These models assess genomic context, desired mutation type, on-target and off-target scores, and potential off-target locations. AI models help improve various genome editing methods, such as base, prime, and epigenome editing, which allow for precise and intentional changes to DNA sequences without the need for donor DNA templates. Furthermore, integrating AI with genome editing and precision medicine enables the creation of personalized treatments tailored to each individual’s unique genetic profile. Examining genomic data enables the identification of mutations, variations, and biomarkers linked to diseases like cancer, diabetes, and Alzheimer’s disease. Integrating AI with genome editing can potentially enhance genetic modification techniques’ precision, efficiency, and cost-effectiveness. Furthermore, it presents novel prospects for fields like genetics, biomedicine, and healthcare, which could significantly impact human health. However, several challenges still exist, including high costs, inaccurate edits, and effective delivery methods for CRISPR components, improved editing performance, and safety in clinical applications.