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从湿实验室过渡到人工智能:对CRISPR中的AI预测因子进行系统审查

Ahtisham Fazeel Abbasi^1,2, Muhammad Nabeel Asim³, Andreas Dengel^4,3

¹Smart Data and Knowledge Services, German Research Center for Artificial Intelligence, 67663, Kaiserslautern, Germany. ahtisham.abbasi@dfki.de.

Journal of translational medicine

|February 5, 2025

概括

这篇论文弥合了人工智能 (AI) 和CRISPR-Cas9基因组编辑之间的知识差距. 它为人工智能研究人员提供了生物学见解,并详细介绍了80个数据集,以改进人工智能驱动的CRISPR-Cas9应用程序.

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.
人工智能的人工智能

背景情况:

CRISPR-Cas9是一种革命性的基因组编辑工具,具有针对性治疗的潜力.
CRISPR-Cas9涉及复杂的多步骤过程,需要精确的gRNA设计,Cas蛋白选择和活动评估.
目前对CRISPR-Cas9的AI预测器的性能有限,原因是AI和CRISPR领域之间的知识差距.

研究的目的:

为了弥合人工智能和CRISPR-Cas9研究之间的知识差距.
为人工智能研究人员提供对CRISPR-Cas9.9生物基础的深入理解.
促进开发更强大,更精确的AI驱动的CRISPR-Cas9应用程序.

主要方法:

对现有的AI预测器和CRISPR-Cas9的预测管道进行分析.
对CRISPR-Cas中的表示学习,机器学习和深度学习趋势的审查9.
编制和呈现80个可用的CRISPR-Cas9系统相关数据集.

主要成果:

鉴定CRISPR-Cas9基因组编辑当前AI预测器的局限性.
洞察50个现有的预测管道的性能值.
通过分析现有方法来开发改进的人工智能预测管道的建议.

结论:

人工智能有效地集成到CRISPR-Cas9中需要跨学科的知识.
这篇论文为CRISPR-Cas9.9的AI研究人员提供了基础的理解和资源.
进一步开发人工智能工具对于优化CRISPR-Cas9的效率和准确性至关重要.

关键词:

由人工智能驱动的CRISPR应用程序抗克里斯普尔蛋白质是一种抗克里斯普尔蛋白质.在CIRSPR上/离目标活动.克里斯普尔的位置和操作子.基因编辑结果的结果在ML/DL和CRISPR的研究中.在CRISPR中进行代表性学习.

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