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Updated: Jun 25, 2026

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size (LEfSe) in Microbiome Data
04:57

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Published on: May 16, 2022

Prevalence aware feature selection improves biomarker identification in microbiome studies.

Ruoxi Yang1, Yingjie Li2, Kris Sankaran3

  • 1Division of Gastroenterology, Hepatology and Nutrition, Department of Internal Medicine, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH 43210, United States.

Bioinformatics (Oxford, England)
|June 24, 2026
PubMed
Summary

A new framework, ParSlet, enhances microbiome biomarker discovery by integrating taxon prevalence with machine learning. This improves the stability and reproducibility of identifying microbial biomarkers for disease diagnosis and targeted therapies.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Published on: October 11, 2018

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Last Updated: Jun 25, 2026

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size (LEfSe) in Microbiome Data
04:57

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

Published on: May 16, 2022

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Microbiome research
  • Computational biology
  • Biomarker discovery

Background:

  • Identifying robust microbial biomarkers is essential for disease diagnosis, understanding biological mechanisms, and developing targeted therapies.
  • Machine learning, particularly random forest, is used for biomarker identification but often yields variable results, limiting practical application.
  • A reliable framework for microbiome biomarker identification is needed.

Purpose of the Study:

  • To propose a prevalence-aware feature selection framework (ParSlet) for reliable microbiome biomarker identification.
  • To incorporate a universal scaling relationship between taxon prevalence and selection frequency into biomarker discovery.
  • To enhance the stability and reproducibility of machine learning-based biomarker identification.

Main Methods:

  • Identified a universal exponential scaling law linking taxon prevalence and biomarker recognition probability.
  • Integrated this scaling law with taxa prevalence into random forest for biomarker identification.
  • Evaluated the ParSlet framework using simulated and real-world microbiome datasets, including colorectal cancer (CRC) data.

Main Results:

  • The integrated approach demonstrated improved feature stability and reproducibility compared to existing methods.
  • ParSlet robustly identified established microbial biomarkers in CRC datasets, such as Ruminococcus, Clostridium_XVIII, and Faecalibacterium.
  • The prevalence-based scaling adjustment enhanced the stability of microbiome biomarker identification.

Conclusions:

  • Integrating prevalence-based scaling into feature importance significantly enhances the stability of microbiome biomarker identification.
  • The ParSlet framework shows promise for more reliable disease diagnostics and uncovering generalizable microbial signatures.
  • This approach can guide the development of targeted microbiome-based interventions.