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Multiple Regression

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...

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Updated: Jan 17, 2026

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

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

Published on: May 16, 2022

Development of multiple microbiome biomarkers using penalized regression methods.

Thi Huyen Nguyen¹, Ibrahim Hamad², Markus Kleinewietfeld²

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

Journal of Applied Microbiology

|September 25, 2025

Summary

This summary is machine-generated.

This study introduces a novel method to identify multiple microbiome biomarkers for predicting health outcomes. Combining multiple taxa significantly improves prediction accuracy for both continuous and binary clinical outcomes.

Keywords:

LASSO/elastic net biomarkers detection microbiome multiple biomarkers penalized regression

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Area of Science:

Microbiome research
Computational biology
Biomarker discovery

Background:

Identifying microbiome biomarkers is crucial for understanding health and disease.
Previous studies focused on single biomarkers, limiting comprehensive analysis.
This research expands on prior work by integrating multiple taxa for biomarker identification.

Purpose of the Study:

To develop and apply a unified approach for identifying multiple microbiome biomarkers.
To enhance the prediction of clinical outcomes using combinations of microbial taxa.
To leverage penalized regression techniques for robust biomarker discovery.

Main Methods:

Utilized LASSO and Elastic Net models within an information theory framework.
Employed Monte Carlo cross-validation for reliable feature selection.
Applied the methodology to a mouse high-salt diet study (continuous outcome) and the CERTIFI Crohn's disease study (binary outcome).

Main Results:

Achieved a high correlation (0.9274) between predicted and observed tumor size in mice using top 5 genera.
Demonstrated a 67.92% reduction in uncertainty for tumor size prediction with the multi-biomarker score.
Significantly improved prediction of Crohn's disease remission using top 5 families as biomarkers.

Conclusions:

Presents a unified penalized regression approach for multiple microbiome biomarker identification.
Successfully applied the method to both continuous and binary clinical outcomes.
Highlights the potential for personalized treatment and improved disease management through enhanced biomarker detection.