X-CRISP:域适应和可解释的CRISPR修复结果预测
Colm Seale1,2, Joana P Gonçalves1
1Pattern Recognition & Bioinformatics, Department of Intelligent Systems, EEMCS Faculty, Delft University of Technology, 2628 XE Delft, The Netherlands.
bioRxiv : the preprint server for biology
|February 20, 2025
概括
新的机器学习模型X-CRISP使用最小序列特征准确预测CRISPR基因编辑结果. 这种可解释的工具通过提高不同细胞类型的预测准确性和概括性来增强CRISPR基因疗法.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 分子生物学分子生物学
背景情况:
- 基因编辑CRISPR的结果对于基因治疗的成功至关重要.
- 基于捐赠者模板的编辑通常是低效的,导致依赖突变性末端连接修复.
- 现有的机器学习模型在预测终端连接维修结果时,难以实现概括性和解释性.
研究的目的:
- 开发一个灵活和可解释的神经网络,X-CRISP,用于预测CRISPR编辑修复结果频率.
- 提高机器学习模型对CRISPR编辑预测的概括性和解释性.
- 利用转移学习来适应模型以适应新的细胞系和实验条件,减少数据需求.
主要方法:
- 开发了X-CRISP,一个使用最小结果和序列特征的神经网络,包括微同学 (MH).
- 评估了X-CRISP的表现,与先前的模型对详细和总体结果预测的表现进行了比较.
- 员工转移学习通过预训练X-CRISP在野生类型小鼠胚胎干细胞 (mESC) 数据上,并将其调整为人类细胞系 (K562,HAP1,U2OS) 和修改的mESC线.
主要成果:
- 在预测详细和总的CRISPR编辑修复结果方面,X-CRISP的表现优于现有的模型.
- 该模型优先考虑微同质 (MH) 位置,而不是像GC内容这样的序列属性,以预测删除结果.
- 适应的X-CRISP模型显示,即使只有50个样本,对目标数据的直接培训也显著改善.
结论:
- X-CRISP提供了一种灵活,可解释和准确的方法来预测CRISPR编辑结果.
- 使用X-CRISP的转移学习使模型能够高效地适应新的细胞类型,大大减少了数据需求.
- 这一策略有望通过改善基因编辑过程的控制和可预测性来推进CRISPR基因疗法.
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