Deep learning–driven prediction of on-target activity, off-target risk, and repair outcomes in CRISPR/Cas9: current landscape and multi-scale perspectives

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Publication Details

Journal
Journal of Translational Medicine
Published
2026-04-28
DOI
https://doi.org/10.1186/s12967-026-08175-1
Primary Topic
CRISPR and Genetic Engineering
Type
article
Field-Weighted Citation Impact
0.00

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article

Deep learning–driven prediction of on-target activity, off-target risk, and repair outcomes in CRISPR/Cas9: current landscape and multi-scale perspectives

Haoyang Zhang, Xiangxing Zhu, Hong Hu, Chao Liu et al.
Journal of Translational Medicine
CRISPR and Genetic Engineering
article

Deep learning–driven prediction of on-target activity, off-target risk, and repair outcomes in CRISPR/Cas9: current landscape and multi-scale perspectives

Haoyang Zhang, Xiangxing Zhu, Hong Hu, Chao Liu, Yangyang Zheng, Linyuan Guo, Tingfeng Zhang, Dongsheng Tang, Ling Chen, Weian Du
article en

Abstract

No abstract available for this paper.
Journal of Translational Medicine
Foshan University (CN), Jinan University (CN), Lund University (SE), Southern University of Science and Technology (CN), Laboratoire de Chémo-biologie synthétique et thérapeutique (FR), Shenzhen Second People's Hospital (CN), Southern Medical University (CN)
National Natural Science Foundation of China
Climate action
Openalex Percentile: Top 17%
CRISPR and Genetic Engineering
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