@article{e8d3df2a-efff-4068-8ff7-5466274ffb8d,
  abstract     = {{The CRISPR/Cas9 system has emerged as a transformative tool in genome editing, playing a pivotal role in enabling precise genetic engineering. Achieving high on-target efficiency while minimizing off-target activity is critical for translating CRISPR/Cas9 into reliable experimental and therapeutic applications. Conventional off-target detection methods are labor-intensive and cost-prohibitive, limiting their scalability. The integration of artificial intelligence has markedly reduced detection costs and substantially increased throughput. Early shallow learning models in the CRISPR/Cas9 domain, although effective in basic classification tasks, exhibited limited feature representation and poor generalization. With advances in algorithms and computational power, deep learning architectures have significantly improved off-target prediction accuracy. However, a critical blind spot remains, most current models operate predominantly at the sequence level, overlooking the downstream functional consequences of genome edits. This review summarizes the current landscape of AI-driven CRISPR/Cas9 prediction methods and proposes a forward-looking “three-layer framework” that integrates molecular, cellular, and tissue dimensions. By linking nucleotide-level edits to protein alterations, cellular functional changes, and tissue-specific responses, this framework aims to bridge the gap between sequence-based predictions and phenotypic outcomes, thereby advancing the precision and translational potential of CRISPR/Cas9 technologies.}},
  author       = {{Du, Weian and Zhang, Tingfeng and Guo, Linyuan and Zheng, Yangyang and Zhang, Haoyang and Zhu, Xiangxing and Tang, Dongsheng and Hu, Hong and Chen, Ling and Liu, Chao}},
  issn         = {{1479-5876}},
  language     = {{eng}},
  publisher    = {{BioMed Central (BMC)}},
  series       = {{Journal of Translational Medicine}},
  title        = {{Deep learning–driven prediction of on-target activity, off-target risk, and repair outcomes in CRISPR/Cas9: current landscape and multi-scale perspectives}},
  url          = {{http://dx.doi.org/10.1186/s12967-026-08175-1}},
  doi          = {{10.1186/s12967-026-08175-1}},
  year         = {{2026}},
}