We developed machine learning models, PRIDICT2.0 and ePRIDICT, to predict prime editing efficiency, offering a robust tool for optimizing genome editing strategies across diverse chromatin contexts.
PRIDICT is a machine learning model that accurately predicts prime editing efficiency, validated across diverse genetic edits and various experimental conditions.