Predicting sequence dropout in chemical mapping experiments - Hamish Blair

Описание к видео Predicting sequence dropout in chemical mapping experiments - Hamish Blair

Sequence dropout, a critical yet poorly understood phenomenon, can drastically reduce data quality in chemical mapping experiments in a sequence-dependent manner. This talk introduces RibonazaNet-Drop, a copy of RibonanzaNet fine-tuned for the pre-experimental prediction of data quality of individual sequences. Preliminary experimental results demonstrate that stratifying the initial sequence library based on the predictions of RibonazaNet-Drop enhances the quality of chemical mapping data obtained.

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