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。

基于注意力的多任务学习用于碱基编辑器结果预测

Attention-based Multi-task Learning for Base Editor Outcome Prediction

人类遗传疾病常常源于点突变，因此需要精确的基因组编辑技术。其中，碱基编辑作为一种使单个核苷酸发生靶向变化的技术显著突出。然而，其临床应用受到编辑效率低和非预期变异的限制，需要在实验室大量试错实验来加快该过程。为了加速这一过程，我们提出了一种基于注意力机制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概率。我们进一步提出了一种多任务学习的模式，能够同时学习多个碱基编辑器（即变体）。我们模型的预测与多个数据集和碱基编辑器变体的实际实验结果能够保持强相关性。这些结果进一步验证了模型改进和加速碱基编辑设计过程的能力。