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通过单个样本马尔科夫流来确定癌症的临界状态

Juntan Liu¹, Yuan Tao¹, Ruoqi Lan¹

¹School of Mathematics, South China University of Technology, Guangzhou, Guangdong Province, China.

|July 31, 2023

概括

一种新的单样马尔科夫流 (sMFE) 方法可以准确地检测复杂疾病中的疾病前状态. 这种方法还确定了新的乐观和悲观的sMFE生物标志物,用于个性化癌症预后.

科学领域:

计算生物学计算生物学
网络医学网络医学
生物统计学生物统计学

背景情况:

复杂的疾病表现出从健康到病态的关键过渡.
早期发现这些关键转变对于及时治疗患者至关重要.
传统方法在有限的临床数据,特别是单样数据集方面扎.

研究的目的:

开发一种无模型计算方法,使用单个样本数据识别复杂疾病的临界状态.
用网络来描述动态疾病变化的特征.
为了能够及早检测疾病前的状态.

主要方法:

引入了单个样本马尔科夫流 (sMFE),一种无模型计算技术.
应用SMFE从网络的角度分析复杂的疾病动态.
在单个样本数据上验证了该方法,绕过了传统的多样本要求.

主要成果:

在来自癌症基因组图谱 (TCGA) 的四个癌症数据集中成功确定了疾病恶化之前的关键状态.
发现了两个新的预后生物标志物:乐观的sMFE (O-sMFE) 和悲观的sMFE (P-sMFE).
证明了O-sMFE和P-sMFE生物标志物的瘤预后评估能力.

结论:

在多种癌症中,sMFE方法准确地检测出疾病前状态.
新的O-sMFE和P-sMFE生物标志物促进了个性化的患者预后.
这一突破对复杂疾病的诊断和治疗有重大影响.

关键词:

临界状态临界状态关键的过渡关键的过渡动态网络生物标志物 (DNB)预测生物标志物预测生物标志物单个样本马尔科夫流 (sMFE)

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