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一个基于神经网络的框架,以了解与2型糖尿病有关的人类肠道微生物组的改变

Shun Guo^1,2,3,4, Haoran Zhang^1,2,3, Yunmeng Chu^1,2,3

¹Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China.

|June 13, 2024

概括

我们使用一种新的计算框架确定了2型糖尿病 (T2D) 的微生物标记物. 禁食血糖显著影响T2D发展中的肠道微生物组变化.

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.
转基因组学是指转基因组学.

背景情况:

人的肠道微生物群在健康和疾病中起着至关重要的作用.
识别与2型糖尿病 (T2D) 等疾病相关的特定微生物变化是一项挑战.
了解这些变化是开发新型诊断和治疗策略的关键.

研究的目的:

开发一个强大的框架来识别与T2D相关的微生物生物标志物.
分析这些生物标志物在T2D病原发生中的作用.
研究肠道微生物组变化与宿主代谢因素之间的关系.

主要方法:

开发了一个结合神经网络 (NN) 和随机森林模型的计算框架.
分析了来自185名健康和183名T2D患者样本的元基因组数据.
鉴定了标记物种和基因,并进行了相互作用网络和回归分析.

主要成果:

确定了40种标记物种和90个与T2D相关的标记基因.
该NN模型在分类T2D样本方面表现出高准确性.
禁食血糖被确定为影响T2D相关肠道微生物组变化的最重要因素 (p <0.05).
标记物种在病例和对照之间表现出稳定性,但在T2D阶段转移.

结论:

开发的框架有效地识别了与疾病相关的微生物生物标志物.
这些生物标志物在T2D的发展和进展中起着重要作用.
这项研究强调了T2D中宿主新陈代谢和肠道微生物组之间的相互作用.

关键词:

与T2D相关的微生物标记物人的肠道微生物群神经网络的神经网络的神经网络随机的森林随机的森林

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