# Guide-Guard: Off-Target Predicting in CRISPR Applications

arXiv: https://arxiv.org/abs/2602.16327 · HTML: https://mlsanity.com/paper/2602.16327/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 8 references that are in the map.
- First posted on arXiv 18 Feb 2026.

## Built on by (most cited first, 1 of 1)

- [A Multimodal Framework for Aligning Human Linguistic Descriptions with Visual Perceptual Data](https://mlsanity.com/paper/2602.19562/) (2026)

## Abstract (arXiv)

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 textitGuide-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.