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一个人工智能驱动的框架,以提高CRISPR-Cas基因编辑作物的监管精度和效率:挑战,机遇和全球协调

Feng Zhu¹, Zihan Liu¹, Zeyu Zheng²

¹School of Medicine, Pingdingshan University, Pingdingshan, Henan, China.

Frontiers in plant science

|February 23, 2026

概括

由于过时的框架,CRISPR-Cas在作物中的基因编辑面临监管挑战. 一种新的计算方法,GeneRegAlignNet与CAPI,使生物编辑与全球法规保持一致,以提高合规性和协调性.

科学领域:

农业生物技术农业生物技术
生物信息学是一种生物信息学.
监管科学监管科学

背景情况:

在作物改进方面,基因编辑的CRISPR-Cas进步已经超过了全球监管框架.
现有的方法难以将生物编辑转化为跨司法管辖区的合规表示.
这在对基因编辑生物的监督中造成了不一致.

研究的目的:

开发一个统一的计算框架,使基因编辑特征与监管政策保持一致.
解决需要的方法,弥合生物意图和监管合规性的需求.
提高基因编辑作物监督的监管解释性和技术准确性.

主要方法:

一个使用GeneRegAlignNet和约束意识政策诱导 (CAPI) 的统一计算框架.
GeneRegAlignNet嵌入了监管语义,用于将基因编辑特征与政策描述符对齐.
CAPI结合了风险校准的政策优化,以处理政策演变和监管差异.

主要成果:

经验验证表明,监管调整的准确性和对政策漂移的弹性得到了改善.
该框架成功地将分子编辑与动态的,跨司法管辖区的政策推断结合起来.
在各种基因编辑场景中展示了增强的监管精度.

结论:

拟议的框架为基因编辑的监管合规提供了一个可扩展和可解释的解决方案.
它促进了对CRISPR-Cas编辑作物的监督的全球协调.
将编辑的正式表示与动态政策推断集成在一起,以提高精度.

关键词:

这就是CRISPR-Ca.限制意识政策诱导 (CAPI) 战略基因规则AlignNet模式符合生物安全的合规性.用基因编辑作物的作物.

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