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通过局部网络确定复杂疾病的关键状态瓦瑟斯坦距离

Changchun Liu¹, Pingjun Hou², Lin Feng¹

¹School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, 471000, China.

Scientific reports

|March 21, 2025

概括

这项研究引入了一种新方法,即局部网络瓦瑟斯坦距离 (LNWD),用于检测复杂疾病的早期迹象. LNWD有助于识别不稳定的疾病前状态,以便及时进行医疗干预.

科学领域:

计算生物学是一种计算生物学.
系统生物学系统生物学
生物医学数据分析

背景情况:

复杂的疾病表现出不稳定的疾病前状态,这些状态可能是可逆的.
早期发现关键转变对于有效干预和个性化医疗至关重要.

研究的目的:

开发一种无模型的方法来识别复杂疾病中的关键过渡和疾病前状态.
为医疗诊断提供早期预警系统,使用单个样本分析.

主要方法:

提出地方网络瓦瑟斯坦距离 (LNWD),一种新的无模型方法.
使用瓦瑟斯坦距离来量化正常和患病样本之间的统计扰动.
分析单个样本以根据LNWD得分变化识别关键状态.

主要成果:

在六个复杂疾病数据集 (TCGA:KIRP,KIRC,LUAD,ESCA; GEO:GSE2565,GSE13268) 中成功识别了关键状态.
证明了单个样本,基于本地网络的方法的有效性.
LNWD有效地检测出疾病前的状态,表明了关键的转变.

结论:

LNWD方法为早期发现复杂疾病提供了强大的工具.
这种方法为医学诊断提供了有价值的早期预警信号.
伦敦北北部疾病在推进个性化疾病诊断和管理方面具有显著的潜力.

关键词:

复杂的疾病复杂的疾病.临界状态临界状态当地网络当地网络疾病前的状态.一个单一的样本样本.瓦斯斯坦的距离是瓦斯斯坦的距离

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