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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| Accès en ligne: | https://arxiv.org/abs/2602.16327 |
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| _version_ | 1866908839564017664 |
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| author | Bingham, Joseph Arussy, Netanel Zonouz, Saman |
| author_facet | Bingham, Joseph Arussy, Netanel Zonouz, Saman |
| contents | With the introduction of cyber-physical genome sequencing and editing technologies, such as CRISPR, researchers can more easily access tools to investigate and create remedies for a variety of topics in genetics and health science (e.g. agriculture and medicine). As the field advances and grows, new concerns present themselves in the ability to predict the off-target behavior. In this work, we explore the underlying biological and chemical model from a data driven perspective. Additionally, we present a machine learning based solution named \textit{Guide-Guard} to predict the behavior of the system given a gRNA in the CRISPR gene-editing process with 84\% accuracy. This solution is able to be trained on multiple different genes at the same time while retaining accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_16327 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Guide-Guard: Off-Target Predicting in CRISPR Applications Bingham, Joseph Arussy, Netanel Zonouz, Saman Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition I.2.1 With the introduction of cyber-physical genome sequencing and editing technologies, such as CRISPR, researchers can more easily access tools to investigate and create remedies for a variety of topics in genetics and health science (e.g. agriculture and medicine). As the field advances and grows, new concerns present themselves in the ability to predict the off-target behavior. In this work, we explore the underlying biological and chemical model from a data driven perspective. Additionally, we present a machine learning based solution named \textit{Guide-Guard} to predict the behavior of the system given a gRNA in the CRISPR gene-editing process with 84\% accuracy. This solution is able to be trained on multiple different genes at the same time while retaining accuracy. |
| title | Guide-Guard: Off-Target Predicting in CRISPR Applications |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition I.2.1 |
| url | https://arxiv.org/abs/2602.16327 |