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Updated: Jun 22, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Biomarker identification by interpretable maximum mean discrepancy.

Michael F Adamer^1,2, Sarah C Brüningk^1,2,3, Dexiong Chen^1,2,4

¹Department of Biosystems Science and Engineering, ETH Zurich, Basel 4056, Switzerland.

Bioinformatics (Oxford, England)

|June 28, 2024

Summary

This summary is machine-generated.

This study introduces SpInOpt-MMD, a novel method for identifying biomarkers in high-dimensional data. It effectively performs two-sample testing and feature selection simultaneously, outperforming existing methods in various applications.

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Area of Science:

Biomedical data analysis
Bioinformatics
Machine learning

Background:

Biomedical research often involves comparing paired sample groups (e.g., treated vs. control) using high-dimensional omics data.
Identifying discriminating features, or biomarkers, is crucial for understanding biological differences.
Current methods often separate two-sample testing and feature selection, limiting integrated analysis.

Purpose of the Study:

To develop a unified statistical framework for simultaneous two-sample testing and feature selection.
To introduce a sparse, interpretable, and optimized Maximum Mean Discrepancy (MMD) test (SpInOpt-MMD).
To demonstrate the versatility and effectiveness of SpInOpt-MMD across diverse data types.

Main Methods:

Developed the SpInOpt-MMD algorithm, integrating multivariate two-sample testing with sparse feature selection.
Applied SpInOpt-MMD to synthetic and real-world datasets, including images, gene expression, and text data.
Compared SpInOpt-MMD performance against established feature selection techniques like SHapley Additive exPlanations and univariate association analysis.

Main Results:

SpInOpt-MMD successfully performs simultaneous two-sample testing and feature selection.
The method demonstrates effectiveness in identifying relevant features, even with small sample sizes.
SpInOpt-MMD outperformed alternative feature selection methods in several experimental comparisons.

Conclusions:

SpInOpt-MMD offers a powerful and versatile approach for biomarker discovery in high-dimensional biomedical data.
The integrated method provides both statistical significance testing and interpretable feature identification.
The developed method enhances the analysis of complex biological datasets, particularly in scenarios with limited samples.