微生物组的系统纵向研究:将时间空间维度与因果和深度学习模型相结合
Liugen Wang1, Guanpeng Qi1, Yongle Shi2
1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, 214122, China.
BMC genomics
|November 21, 2025
概括
深度学习框架SysLM通过解决缺失值并使性结肠炎等疾病的生物标志物发现,改善了纵向微生物组数据的分析.
科学领域:
- 微生物组研究的研究.
- 计算生物学是一种计算生物学.
- 系统生物学 系统生物学
背景情况:
- 纵向微生物组数据提供了有关疾病期间宿主微生物动态的见解.
- 挑战包括缺失的值,稀疏性和可解释性,阻碍生物标志物发现和疾病建模.
研究的目的:
- 介绍SysLM,这是一个深度学习框架,用于对纵向微生物群数据进行系统分析.
- 解决缺失的值,并增强生物标志物发现,以改善疾病建模.
主要方法:
- SysLM-I模块:使用元数据,功能增强,时间卷积网络和双向长短期内存进行缺失值赋值.
- SysLM-C模块:将深度学习与因果推断集成在一起,用于分类和生物标志物查 (差异性,网络,核心,动态,疾病特异性,共享).
主要成果:
- 在多个数据集的归算,分类和生物标志物发现方面,SysLM表现出卓越的性能.
- 鉴定了性结肠炎中的新型微生物机制,证明了精准医学的潜力.
结论:
- SysLM为分析复杂的纵向微生物组数据提供了一个强大的框架.
- 将深度学习与因果建模相结合,推动了基于微生物组的疾病研究和治疗策略.
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