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机器学习集成识别了急性山病的八基因诊断签名

Dan Yang^1,2, Xinyao Yin³, Qian Li^1,2

¹General Hospital of Xinjiang Military Command, Urumqi, China.

Frontiers in medicine

|December 4, 2025

概括

研究人员开发了一种机器学习模型,使用血液生物标志物来诊断急性山病 (AMS). 这为早期AMS检测提供了一种实用的方法,特别是在高海拔地区.

科学领域:

发现生物标志物的发现.
基因组学就是基因组学.
高空医学是一种高度医学.

背景情况:

急性山病 (AMS) 影响25-90%的人在高海拔地区.
目前的AMS诊断依赖于主观的自我报告问卷.
对于用于AMS检测的客观和可靠的生物标志物有着至关重要的需求.

研究的目的:

建立急性山病 (AMS) 的诊断模型.
通过先进的测序和机器学习技术,识别可靠的血液生物标志物用于AMS诊断.

主要方法:

使用单细胞RNA测序 (scRNA-seq) 和散装RNA测序 (RNA-seq) 来识别AMS相关基因.
使用机器学习算法与12个选的AMS相关基因构建了一个诊断模型.
使用培训和外部队列验证了模型的诊断准确性,并通过定量PCR (qPCR) 确认了基因表达.

主要成果:

在AMS进展中确定了髓质细胞和血小板细胞的丰富.
使用集成scRNA-seq和大量RNA-seq数据选了12个关键的AMS相关基因.
开发了一个机器学习模型 (Stepglm + Naive Bayes) 的AUC为0.948 (训练) 和验证的性能 (AUC为0.818和0.760).
表观遗传学分析表明通过基因素和m6A甲基化进行调节,途径分析表明参与免疫信号和氧化应激.

结论:

通过机器学习成功识别和验证了用于AMS诊断的最小血液生物标志物签名.
这种方法为早期AMS检测提供了一个实用的工具,特别有利于资源有限的高海拔群体.

关键词:

有关急性山病的疾病.诊断签名诊断签名签名机器学习是机器学习.个性化医疗是个性化的医疗.一个单细胞RNA-seqq.

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