单细胞和表观遗传数据的可解释深度学习揭示了对衰老的新型分子洞察力
Zhi-Peng Li1,2, Zhaozhen Du3, De-Shuang Huang4
1Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, 315201, Zhejiang, China.
Scientific reports
|February 11, 2025
概括
深度学习 (DL) 和可解释的人工智能 (XAI) 揭示了分子衰老模式. 这种方法确定了与衰老相关的新型核糖体和炎症途径,传统方法错过了这些途径.
科学领域:
- 计算生物学是一种计算生物学.
- 人工智能在衰老研究中的研究.
- 奥米克斯数据分析数据分析.
背景情况:
- 深度学习 (DL) 和可解释的人工智能 (XAI) 是用于识别复杂数据模式的先进机器学习工具.
- 在分子层面研究生物衰老需要分析大型的奥米克数据集.
- 现有的方法可能无法捕捉微妙的分子衰老特征.
研究的目的:
- 将DL和XAI应用于大型omics数据集,以研究分子衰老.
- 开发一种用于构建和解释分子衰老时钟的新框架.
- 为了发现标准机器学习所遗漏的与衰老相关的新型分子模式.
主要方法:
- 开发了一个多视图图表级表示学习 (MGRL) 框架,集成生物网络信息.
- 将DL-XAI框架应用于一个大型单细胞转录组数据集 (来自981位捐赠者的100多万个免疫细胞).
- 将DL-XAI框架应用于排序单细胞的DNA甲基化数据.
主要成果:
- 在免疫细胞中识别出与年龄相关的核糖体基因子网络,独立于细胞类型.
- 揭示了在单细胞中随着年龄的增长而增加的表观遗传放松的炎症反应途径.
- 证明这些发现是DL-XAI方法独有的,不能通过标准方法发现.
结论:
- 开发的DL-XAI计算框架有效地揭示了对衰老的新生物学见解.
- 这种方法增强了分子衰老特征的发现,例如核糖体和炎症通路.
- 结合DL和XAI进行先进的分子衰老研究和生物标志物发现.
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