Mechanistic machine learning for prediction of prime editing outcomes

Mechanistic machine learning integrates biological principles into model architectures to move beyond black-box predictive limitations. This advancement significantly improves the precision of genome editing tools by accurately forecasting complex biochemical outcomes.