Guide-Guard: Off-Target Predicting in CRISPR Applications
Joseph Bingham Netanel Arussy Saman Zonouz
作者信息
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.引用本文复制引用
Joseph Bingham,Netanel Arussy,Saman Zonouz.Guide-Guard: Off-Target Predicting in CRISPR Applications[EB/OL].(2026-02-18)[2026-07-27].https://arxiv.org/abs/2602.16327.学科分类
生物科学理论、生物科学方法/生物科学研究方法、生物科学研究技术/生物化学/分子生物学
|国家预印本平台