AI提升CRISPR引导RNA设计效率与安全性，解释性模型揭示关键序列特征。
Artificial Intelligence for CRISPR Guide RNA Design: Explainable Models and Off-Target Safety
- 利用深度学习预测gRNA靶向活性，识别潜在脱靶风险。
- 结合XAI技术解析模型决策依据，定位影响编辑效果的序列元件。
- 适合基因编辑、AI生物应用及临床转化研究者参考。
基于CRISPR的基因编辑已革新生物技术，但优化引导RNA（gRNA）设计以提高效率与安全性仍是关键挑战。2020至2025年进展表明，人工智能（尤其是深度学习）可显著提升gRNA靶向活性预测能力，并有效识别脱靶风险。同时，新兴的可解释人工智能（XAI）技术逐步揭开模型黑箱，揭示驱动Cas酶性能的序列特征与基因组背景。本文综述当前先进机器学习模型在CRISPR gRNA设计中的应用，重点分析模型预测的解释策略，以及脱靶预测与安全评估的新发展。强调来自顶刊的研究突破，体现AI与基因编辑的跨学科融合，推动更高效、精准且具临床可行性的CRISPR应用。
原文摘要 · Abstract (English)
CRISPR-based genome editing has revolutionized biotechnology, yet optimizing guide RNA (gRNA) design for efficiency and safety remains a critical challenge. Recent advances (2020--2025, updated to reflect current year if needed) demonstrate that artificial intelligence (AI), especially deep learning, can markedly improve the prediction of gRNA on-target activity and identify off-target risks. In parallel, emerging explainable AI (XAI) techniques are beginning to illuminate the black-box nature of these models, offering insights into sequence features and genomic contexts that drive Cas enzyme performance. Here we review how state-of-the-art machine learning models are enhancing gRNA design for CRISPR systems, highlight strategies for interpreting model predictions, and discuss new developments in off-target prediction and safety assessment. We emphasize breakthroughs from top-tier journals that underscore an interdisciplinary convergence of AI and genome editing to enable more efficient, specific, and clinically viable CRISPR applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。