Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens
Kim Fabiano Marquart, Ahmed Allam, Sharan Janjuha, Anna Sintsova, Lukas Villiger, Nina Frey, Michael O. Krauthammer, Gerald Schwank
This paper develops BE-DICT, a deep learning model trained on high-throughput screens to predict base editing outcomes with high accuracy.
Nature Communications · Aug 25, 2021Peer-reviewedGenomics