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

Jiecong Lin, Xingjian Chen and Ka-Chun Wong ()
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Jiecong Lin: City University of Hong Kong
Xingjian Chen: City University of Hong Kong
Ka-Chun Wong: City University of Hong Kong

Statistics in Biosciences, 2023, vol. 15, issue 3, No 7, 657-668

Abstract: 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 ).

Keywords: CRISPR; Gene editing; Off-target prediction; Deep learning (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s12561-022-09352-8

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