Human genetic diseases often arise from point mutations, emphasizing the
critical need for precise genome editing techniques. Among these, base editing
stands out as it allows targeted alterations at the single nucleotide level.
However, its clinical application is hindered by low editing efficiency and
unintended mutations, necessitating extensive trial-and-error experimentation
in the laboratory. To speed up this process, we present an attention-based
two-stage machine learning model that learns to predict the likelihood of all
possible editing outcomes for a given genomic target sequence. We further
propose a multi-task learning schema to jointly learn multiple base editors
(i.e. variants) at once. Our model's predictions consistently demonstrated a
strong correlation with the actual experimental results on multiple datasets
and base editor variants. These results provide further validation for the
models' capacity to enhance and accelerate the process of refining base editing
designs.

提出了一种基于注意力的两阶段机器学习模型，用于预测给定基因组目标序列的所有可能编辑结果的可能性，并采用多任务学习模式同时学习多个碱基编辑器变体，验证了该模型对于增强和加速精细化碱基编辑设计进程的能力。