Deep learning–driven prediction of on-target activity, off-target risk, and repair outcomes in CRISPR/Cas9: current landscape and multi-scale perspectives
Du, Weian ; Zhang, Tingfeng ; Guo, Linyuan ; Zheng, Yangyang ; Zhang, Haoyang LU
; Zhu, Xiangxing
; Tang, Dongsheng
; Hu, Hong
; Chen, Ling
and Liu, Chao
(2026)
In Journal of Translational Medicine
- 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/e8d3df2a-efff-4068-8ff7-5466274ffb8d
- author
- Du, Weian
; Zhang, Tingfeng
; Guo, Linyuan
; Zheng, Yangyang
; Zhang, Haoyang
LU
; Zhu, Xiangxing
; Tang, Dongsheng
; Hu, Hong
; Chen, Ling
and Liu, Chao
- organization
- publishing date
- 2026
- type
- Contribution to journal
- publication status
- epub
- subject
- in
- Journal of Translational Medicine
- publisher
- BioMed Central (BMC)
- external identifiers
-
- pmid:42046128
- ISSN
- 1479-5876
- DOI
- 10.1186/s12967-026-08175-1
- language
- English
- LU publication?
- yes
- id
- e8d3df2a-efff-4068-8ff7-5466274ffb8d
- date added to LUP
- 2026-05-05 21:55:36
- date last changed
- 2026-05-06 07:23:21
@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}},
}