  CRISPR-Cas13 is a system that utilizes single stranded RNAs for RNA editing.
Prediction of on-target and off-target effects for the CRISPR-Cas13d dependency
enables us to design specific single guide RNAs (sgRNAs) that help locate the
desired RNA target positions. In this study, we compared the performance of
multiple machine learning algorithms in predicting these effects using a
reported dataset. Our results show that Catboost is the most accurate model
with high sensitivity. This finding represents a significant advancement in our
understanding of how to chose modeling methods to deal with RNA sequence
feaatures effictivelys. Furthermore, our approach can potentially be applied to
other CRISPR systems and genetic engineering techniques. Overall, this work has
important implications for developing safer and more effective gene therapies
and biotechnological applications.
