Guide-Guard: Off-Target Predicting in CRISPR Applications
This addresses the challenge of off-target prediction in CRISPR applications for genetics and health science, representing an incremental improvement with a specific method.
The paper tackles the problem of predicting off-target behavior in CRISPR gene-editing by developing a machine learning solution called Guide-Guard, which achieves 84% accuracy in predicting system behavior given a gRNA and can be trained on multiple genes simultaneously.
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.