在多omics网络上的平衡深度学习识别了病态大脑衰老的分子子组
medRxiv : the preprint server for health sciences
|February 27, 2026
概括
这项研究使用了新的网络信息多omics集成框架,确定了5个不同的脑衰老分子子组. 这些子组揭示了依赖阶段的生物模式,有助于理解阿尔茨海默病的异质性.
科学领域:
- 神经科学是一个神经科学.
- 基因组学就是基因组学.
- 计算生物学 计算生物学
背景情况:
- 神经退行性疾病,如阿尔茨海默氏症 (AD) 呈现出显著的异质性,使诊断和治疗复杂化.
- 高维度,不平衡的多学科数据与生物网络的整合是一个关键的方法挑战.
研究的目的:
- 开发和验证基于网络的多omics集成框架,用于识别大脑衰老中的分子子组.
- 描述已识别的子组中的分子和神经病理差异.
主要方法:
- 开发了一个框架,将多omics数据 (转录组学,蛋白质组学,代谢组学) 与来自ROS/MAP队伍的大脑网络集成在一起.
- 利用图形嵌入 (AD Atlas) 和自动编码器来导出用于等级聚类的多omics表达式得分.
- 在独立的队列中验证了子组稳定性和分类器性能.
主要成果:
- 确定了五个具有明显认知表现和神经病理特征的分子子组.
- 使用转录基因和蛋白质基因数据的交叉验证分类来证明可靠的子组歧视.
- 揭示了依赖阶段的生物模式,包括突触/免疫激活,线粒体功能障碍和蛋白质静止功能障碍.
结论:
- 该框架成功识别了大脑衰老的分子子组,提供了超越临床诊断的见解.
- 观察到一系列的疾病进展,从有风险的对照到典型的阿尔茨海默病,由tau病理学区分开来.
- 确定的子组包括参考对照组,混合病理组和AD进展阶段.
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