Guide-Guard: Off-Target Predicting in CRISPR Applications

• URL: http://arxiv.org/abs/2602.16327v1
• Date: Wed, 18 Feb 2026 10:06:54 GMT
• Title: Guide-Guard: Off-Target Predicting in CRISPR Applications
• Authors: Joseph Bingham, Netanel Arussy, Saman Zonouz,
• Abstract summary: We explore the underlying biological and chemical model from a data driven perspective.<n>We present a machine learning based solution named textitGuide-Guard to predict the behavior of the system given a gRNA in the CRISPR gene-editing process with 84% accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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