![[2602.16327] Guide-Guard: Off-Target Predicting in CRISPR Applications](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.16327] Guide-Guard: Off-Target Predicting in CRISPR Applications
Summary
The paper presents Guide-Guard, a machine learning solution designed to predict off-target effects in CRISPR applications with 84% accuracy, enhancing the reliability of gene editing.
Why It Matters
As CRISPR technology advances, the ability to predict off-target effects is crucial for ensuring the safety and efficacy of genetic modifications. Guide-Guard addresses this challenge, providing a data-driven approach that can improve research outcomes in genetics and health sciences.
Key Takeaways
- Guide-Guard predicts off-target effects in CRISPR with 84% accuracy.
- The solution can be trained on multiple genes simultaneously.
- It enhances the reliability of CRISPR applications in genetics and health.
- The approach is rooted in a biological and chemical model.
- This research contributes to the growing field of safe gene editing.
Computer Science > Machine Learning arXiv:2602.16327 (cs) [Submitted on 18 Feb 2026] Title:Guide-Guard: Off-Target Predicting in CRISPR Applications Authors:Joseph Bingham, Netanel Arussy, Saman Zonouz View a PDF of the paper titled Guide-Guard: Off-Target Predicting in CRISPR Applications, by Joseph Bingham and Netanel Arussy and Saman Zonouz View PDF HTML (experimental) 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. Comments: Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) ACM classes: I.2.1 Cite as: arXiv:2602.16327 [cs.LG] (or arXiv:2602.16327v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2602.16327 Focus to learn more arXiv-issued DOI via DataCite (pendin...
