EpInflammAge:基于深度学习的疾病相关生物衰老的表观遗传炎症时钟
Alena Kalyakulina1,2, Igor Yusipov1,2, Arseniy Trukhanov3
1Artificial Intelligence Research Center, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod 603022, Russia.
International journal of molecular sciences
|July 12, 2025
概括
EpInflammAge集成了表观遗传和炎症标志物,以准确预测生物年龄. 这种可解释的人工智能工具对老龄化和疾病的研究和临床应用具有前景.
科学领域:
- 衰老的研究研究.
- 计算生物学是一种计算生物学.
- 免疫学 免疫学 免疫学
背景情况:
- 衰老的特点是表观遗传变化和免疫衰老.
- 准确预测生物年龄对于了解健康和疾病至关重要.
- 现有的表观遗传时钟在疾病敏感性方面存在局限性.
研究的目的:
- 开发EpinflammAge,这是一个可解释的深度学习工具,用于生物年龄预测.
- 整合表观遗传和炎症标志物,以提高准确性和疾病敏感性.
- 为了弥合衰老的标志:表观遗传变化和免疫衰老.
主要方法:
- 利用深度神经网络进行表式数据分析.
- 综合了参与者的表观遗传 (DNA甲基化) 和炎症 (细胞因子) 数据.
- 使用开源表观遗传数据生成合成炎症生物标志物.
- 使用组合数据集训练了一个年龄估计模型.
主要成果:
- EpInflammAge实现了与34个表观遗传时钟模型相比的竞争性性能.
- 在健康对照中,平均绝对误差为7年,Pearson相关性为0.85.
- 在多种疾病类别中表现出强大的敏感性.
- 可解释的人工智能确定了特征对年龄预测的贡献.
结论:
- 结合表观遗传和炎症概况,可以提高生物年龄预测对疾病的敏感性.
- EpInflammAge为研究和临床应用提供了一个有前途的工具.
- 该工具的可解释性质有助于理解预测因素.
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