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使用单样样本网络 (SNE) 检测生物发育过程中的过渡前阶段

Chengmu She¹, Zhirui Tang¹, Yuan Tao²

¹School of Mathematics, Foshan University, Foshan, China.

NPJ systems biology and applications

|November 22, 2025

概括

一个新的算法,单样样本网络 (SNE),可以在单个样本水平上识别生物系统中关键的过渡前阶段. 这种方法有助于早期疾病诊断和预后,通过在灾难性事件发生之前检测微妙的变化.

科学领域:

计算生物学计算生物学
系统生物学系统生物学
生物医学数据科学生物医学数据科学

背景情况:

复杂的生物系统在关键转变之前表现出过渡前的阶段.
在单个样本或单个细胞水平上识别这些关键状态对于及时干预至关重要.
传统的统计方法在临床环境中常见的小样本大小方面扎.

研究的目的:

开发一种新的算法,用于在个体样本水平上识别过渡前阶段.
为了量化与参考数据相对的样本特异性干扰.
为了能够在复杂的生物过程中早期检测到关键状态.

主要方法:

介绍单个样本网络 (SNE),这是一个用于临界状态分析的新算法.
将SNE应用于数值模拟和各种现实数据集 (流感,单细胞,瘤数据).
确定乐观的SNE (O-SNE) 和悲观的SNE (P-SNE) 的生物标志物.

主要成果:

在模拟和八个现实世界生物数据集中,SNE成功确定了过渡前的阶段.
与现有的单个样本方法相比,SNE方法显示出更高的性能.
确定了新的预后生物标志物 (O-SNE和P-SNE),并得到了功能信号生物标志物的支持.

结论:

SNE算法提供了一种强大的计算方法,用于在单个样本/单个细胞水平上发现过渡前阶段和生物标志物.
这种方法为早期个性化生物分析提供了新的见解,包括疾病诊断和预后.
这些发现通过揭示复杂生物系统中的关键状态,促进及时干预.

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