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.
Viral testing for pediatric inpatients with respiratory symptoms is common, with considerable associated charges. In an attempt to reduce testing volumes, we studied whether data available at the time of admission could aid in identifying children …