CRISPR-MFH: A Lightweight Hybrid Deep Learning Framework with Multi-Feature Encoding for Improved CRISPR-Cas9 Off-Target Prediction
This study offers a novel perspective for advancing deep learning technology in the realm of CRISPR-Cas9 off-target detection.
Authors
- Quan Zou (ORCID: https://orcid.org/0000-0001-6406-1142)
- Yanyi Zheng (ORCID: https://orcid.org/0009-0006-7968-8574)
- Jian Li (ORCID: https://orcid.org/0000-0003-3320-7924)
- Yanpeng Yang (ORCID: https://orcid.org/0009-0006-2129-5938)
Institutions
- Zhejiang A & F University (CN)
- University of Electronic Science and Technology of China (CN)
- Beijing Forestry University (CN)
- Quzhou University (CN)
Publication Details
- Journal
- Genes
- Published
- 2025-03-28
- DOI
- https://doi.org/10.3390/genes16040387
- Citations
- 5
- Primary Topic
- CRISPR and Genetic Engineering
- Type
- article
- Field-Weighted Citation Impact
- 4.03
Funders
- National Natural Science Foundation of China