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An Artificial Intelligence Approach for Gene Editing Off-Target Quantification: Convolutional Self-attention Neural Network Designs and Considerations

Author

Listed:
  • Jiecong Lin

    (City University of Hong Kong)

  • Xingjian Chen

    (City University of Hong Kong)

  • Ka-Chun Wong

    (City University of Hong Kong)

Abstract

In the CRISPR-based gene-editing system, an important issue is the off-target cleavage which could alter the functions of unintended genes and induce toxicity. Numerous biological techniques have been proposed to detect the off-target effects. However, those laboratory-based techniques are expensive and time-consuming for guide RNA selection. Therefore, we introduce a computational method based on convolutional neural network and attention module to predict the CRISPR off-target activity. With two validation experiments, we demonstrate that our proposed model has improved predictive performance over the state-of-the-art deep-learning-based off-target prediction models in terms of Receiver Operating Characteristics and Precision-Recall analyses. For scientific reproducibility, we have made the source code available at the GitHub repository ( https://github.com/JasonLinjc/CRISPRattention ).

Suggested Citation

  • Jiecong Lin & Xingjian Chen & Ka-Chun Wong, 2023. "An Artificial Intelligence Approach for Gene Editing Off-Target Quantification: Convolutional Self-attention Neural Network Designs and Considerations," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(3), pages 657-668, December.
    • Handle: RePEc:spr:stabio:v:15:y:2023:i:3:d:10.1007_s12561-022-09352-8
    DOI: 10.1007/s12561-022-09352-8
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