CRISPR-MFH: A Lightweight Hybrid Deep Learning Framework with Multi-Feature Encoding for Improved CRISPR-Cas9 Off-Target Prediction.

Journal: Genes
PMID: 40282347

Abstract

BACKGROUND: The CRISPR-Cas9 system has emerged as one of the most promising gene-editing technologies in biology. However, off-target effects remain a significant challenge. While recent advances in deep learning have led to the development of models for off-target prediction, these models often fail to fully leverage sequence pair information. Furthermore, as the models' parameter sizes increase, so do their complexities, limiting their practical applicability.

Authors

  • Yanyi Zheng
    College of Landscape Architecture, Beijing Forestry University, Beijing 100083, China.
  • Quan Zou
  • Jian Li
    Fujian Key Laboratory of Traditional Chinese Veterinary Medicine and Animal Health, College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou, China.
  • Yanpeng Yang
    School of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China.

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