Guide-Guard: Off-Target Predicting in CRISPR Applications

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 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.