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GraphAge:释放图形神经网络的力量来解码表观遗传衰老

Saleh Sakib Ahmed¹, Nahian Shabab¹, Abul Hassan Samee²

¹Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, ECE Building, West Palashi, Dhaka 1205, Bangladesh.

|June 30, 2025

概括

这项研究介绍了GraphAge,这是一种新的图形神经网络 (GNN) 模型,通过分析DNA甲基化模式和CpG位点关系来预测表观遗传年龄. GraphAge实现了竞争力的准确性,并提供了对衰老机制的更深入的见解.

科学领域:

表观遗传学在表观遗传学中,表观遗传学是指表观遗传学.
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

基因甲基化是预测生物年龄的关键表观遗传标记.
现有的表观遗传时钟往往忽略了关键的CpG位点相互关系,如同甲基化模式.

研究的目的:

开发一种用于表观遗传年龄预测的新方法,使用甲基化数据的图形表示.
利用图形神经网络 (GNN) 来结合CpG站点的结构和位置信息.
提高表观遗传年龄预测模型的可解释性.

主要方法:

以图形形式表示DNA甲基化数据,CpG位点作为节点,它们的关系 (联合甲基化,基因,染色体) 作为边缘.
使用一个名为GraphAge的图形神经网络 (GNN) 模型进行年龄预测.
使用GNN解释器用于模型解释和洞察力生成.

主要成果:

GraphAge表现出具有竞争力的表现,平均绝对误差为3.207,平均平方误差为25.277.
该模型的表现优于现有的表观遗传年龄预测方法.
GNN解释器揭示了关键的CpG位点和参与衰老的甲基化调节通路.

结论:

GraphAge有效地利用甲基化数据中的结构和位置信息来准确预测年龄.
该模型提供了卓越的解释性,揭示了对衰老生物学的新见解.
GraphAge代表了向多式模式模型的显著进步,以了解衰老的复杂性.

关键词:

通过DNA甲基化.同甲基化共甲基化计算表观遗传学计算表观遗传学表观遗传年龄表观遗传年龄图形神经网络 (GNN) 是一个图形神经网络.

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