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Updated: Sep 9, 2025

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深度学习驱动的蛋白质组学分析用于Renin-Angiotensin系统中的基因注释

Mortaza Eivazi¹, Kamran Hosseini², Shahin Alipanahi¹

¹Department of Computer Science, Faculty of Mathematics, Statistics, and Computer Science, University of Tabriz, Tabriz, Iran.

European journal of pharmacology

|September 4, 2025

概括

这项研究使用深度学习来绘制心血管疾病中的雷宁-血管素系统 (RAS) 基因功能. 它确定了关键基因和新途径作为高血压的潜在治疗点.

科学领域:

心血管生物学
基因组学
医学的人工智能

背景情况:

素-血管素系统 (RAS) 在心血管疾病中起着至关重要的作用,但其许多基因功能仍然不太清楚.
了解这些功能对于开发高血压和心肌病等疾病的有效治疗至关重要.

研究的目的:

开发和验证多标签深度学习模型,以系统地注释RAS基因功能及其生物途径的参与.
确定与心血管疾病发病相关的新型RAS成分和相互作用途径.

主要方法:

使用TF-IDF和PCA处理了39,463个与RAS相关的出版物.
应用多层感知器 (MLP) 进行多标签分类,通过精度,F1分数,排名损失和ROC-AUC来评估性能.
在高血压小鼠模型中使用细胞外膀 (EV) 蛋白质组学和毛细血管西方试验验验证.

主要成果:

深度学习模型实现了高性能 (精度:0.7474,ROC-AUC:0.8697),超过了传统方法.
将基因分为生物分支进一步提高了模型的解释性和性能 (精度:0.8312,ROC-AUC:0.9182).
确定了AGTR2,IRAP,Ywhas,EDNRA和ESR2作为关键的RAS基因,IRAP在高血压中显著上调并与关键信号分子相互作用.

结论:

这项研究首次将多标签人工智能与EV蛋白质组用于RAS途径注释,为理解复杂的基因途径关系提供了强大的框架.
揭示了一种新的IRAP/ Ywha ((s) /Nedd4-2-ACE2相互作用轴,突出显示了高血压和相关心血管疾病的潜在治疗点.

关键词:

高血压机器学习多层感知器素 - 血管素系统

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