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CRISPR-FMC:一种双分支混合网络,用于预测CRISPR-Cas9的目标活动

Chuxuan Li¹, Jian Li¹, Quan Zou^2,3

¹School of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou, China.

Frontiers in genome editing

|September 15, 2025

概括

新型双分支神经网络CRISPR-FMC通过整合序列编码和RNA结构来准确预测sgRNA活动. 这种方法提高了对CRISPR-Cas9应用的多样化数据集和低资源场景的概括性.

科学领域:

计算生物学计算生物学
基因组学就是基因组学.
生物信息学是一种生物信息学.

背景情况:

预测单导向RNA (sgRNA) 在目标上的活性对于CRISPR-Cas9基因编辑效率至关重要.
由于浅层架构和单模编码,现有的模型在数据集,有限的样本和复杂的序列上下文中难以概括.

研究的目的:

开发一个可靠和可解释的深度学习框架,用于预测sgRNA活动.
增强预测模型在各种数据集和低资源条件中的概括能力.

主要方法:

双分支混合神经网络CRISPR-FMC整合了从预训练的RNA-FM模型中嵌入RNA结构的One-hot编码.
采用多尺度卷积,BiGRU和变压器块用于层次特征提取.
采用双向交叉注意力和残留前网络进行多式联接.

主要成果:

在9个公开的CRISPR-Cas9数据集中,CRISPR-FMC的性能始终优于现有方法,实现了卓越的斯皮尔曼和皮尔森相关性.
在低资源和跨数据集预测设置中表现出强的性能.
废弃性研究证实了模块的贡献,分析强调了对PAM近端区域的敏感性.

结论:

克里斯普尔-FMC为预测不同基因组环境中的sgRNA活动提供了一个强大的和可解释的框架.
该模型对PAM近端区域的灵敏度与生物学理解一致,验证了其预测能力.

关键词:

克里斯普尔-Cas9是什么意思这就是RNA-FM.这就是SgRNARNA.深度学习是一种深度学习.在目标活动中的目标活动.

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