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Updated: Jan 17, 2026

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

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Published on: May 16, 2022

使用惩罚性回归方法开发多种微生物组生物标志物.

Thi Huyen Nguyen¹, Ibrahim Hamad², Markus Kleinewietfeld²

¹Data Science Institute, Hasselt University, 3590 Diepenbeek, Belgium.

Journal of applied microbiology

|September 25, 2025

概括

这项研究引入了一种新的方法,用于识别多种微生物群生物标志物,以预测健康结果. 结合多种类型显著提高了连续和二进制临床结果的预测准确性.

关键词:

拉索/弹性网拉索/弹性网拉索/弹性网生物标志物检测生物标志物检测微生物组是一个微生物组.有多种生物标志物.受到惩罚的回归回归.

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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.
发现生物标志物的发现.

背景情况:

识别微生物组生物标志物对于了解健康和疾病至关重要.
以前的研究集中在单个生物标志物上,限制了全面的分析.
这项研究扩展了先前的工作,通过整合多种类型来识别生物标志物.

研究的目的:

开发和应用一种统一的方法来识别多种微生物组生物标志物.
为了提高使用微生物种群组合的临床结果的预测.
为了利用惩罚性回归技术来进行强大的生物标志物发现.

主要方法:

在信息理论框架内利用了LASSO和弹性网模型.
采用蒙特卡洛交叉验证,以确保可靠的功能选择.
将该方法应用于小鼠高盐饮食研究 (连续结果) 和CERTIFI克朗病研究 (二进制结果).

主要成果:

在使用前5个属的小鼠中,预测和观察到的瘤大小之间实现了高相关性 (0.9274).
通过多生物标志物得分,瘤大小预测的不确定性降低了67.92%.
使用前5个家族作为生物标志物显著改善了克罗恩病缓解的预测.

结论:

介绍了用于多种微生物组生物标记物识别的统一惩罚回归方法.
成功地将该方法应用于连续和二进制临床结果.
通过加强生物标志物检测,突出了个性化治疗和改善疾病管理的潜力.