X-CRISP:具有域适应性和可解释性的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.
Bioinformatics advances
|July 18, 2025
概括
新型神经网络X-CRISP准确地预测了CRISPR基因编辑结果,使用微同质学. 使用X-CRISP进行转移学习,可以有效地适应新细胞系的模型,从而改善基因治疗的开发.
科学领域:
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
- 遗传学 是一个遗传学.
背景情况:
- 控制CRISPR编辑结果对于基因疗法的成功至关重要.
- 基于捐赠者模板的编辑通常是低效的,需要替代策略,如突变性末端连接修复.
- 预测端连接维修结果的现有机器学习模型缺乏通用性和解释性.
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.
- 提高机器学习模型在这个领域的概括性和解释性.
- 利用转移学习来有效地适应新细胞系和实验条件的模型.
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CRISPR修复结果频率方面,X-CRISP的表现优于现有的模型.
- 该模型优先考虑了微同质的位置,而不是删除结果的序列属性.
- 经过调整的X-CRISP模型在50个目标数据样本中显示出显著的改善,证明了转移学习的有效性.
结论:
- 对于预测CRISPR编辑结果,X-CRISP提供了一种更准确,更普遍,更易于解释的方法.
- 转移学习显著减少了开发新细胞类型或条件的预测模型的数据需求.
- 这项工作通过改善基于CRISPR的基因工程的控制和可预测性,促进了基因疗法的进步.
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