BE-CORR: Learning to Correct Source-Library Outcomes for Endogenous Base-Editing Prediction

DNA base editors enable targeted single-nucleotide changes and are promising tools for functional genomics and therapeutic genome engineering. Machine learning has become an effective approach for predicting base-editing outcomes, helping prioritize editor-guide designs early in development. Many predictors are trained on integrated or synthetic target-library assays (source libraries), where many targets are measured in controlled, non-native contexts. However, practical editing occurs at endogenous genomic loci, where chromatin and regulatory state can substantially shift outcomes relative to source libraries. Existing approaches typically target either source-library prediction or direct endogenous prediction: the former does not directly transfer to native-locus behavior, while the latter is constrained by scarce endogenous training data. We therefore reformulate endogenous outcome prediction as source-to-endogenous correction: obtain a source outcome profile as a source-library prior, predict a context-induced shift from endogenous features such as chromatin accessibility and transcriptional activity, and recover the endogenous profile as the source profile plus the shift. We introduce BE-CORR (Source-to-Endogenous Correction of Base-Editing Outcomes), an interpretable and robust correction model that represents the source profile as nonnegative editing hazard, rescales overall editing opportunity, and redistributes per-position opportunity through constrained hazard transport with reliability-gated blending. Experiments show that BE-CORR consistently outperforms strong sequence-only, context-aware, and source-using baselines in endogenous endpoint accuracy, ranking, calibration, matched source-to-endogenous transfer, and correction gain. Code and data are available at https://anonymous.4open.science/r/BE-CORR.