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Updated: Jun 15, 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

Machine learning driven biomarker selection for medical diagnosis.

Divyagna Bavikadi¹, Ayushi Agarwal¹, Shashank Ganta¹

¹Fulton Schools of Engineering, Arizona State University, Tempe, Arizona, United States of America.

|June 11, 2025

Summary

This summary is machine-generated.

Selecting the right biomarkers is key for disease diagnosis. This study shows advanced machine learning methods significantly improve accuracy over traditional approaches when identifying disease-related molecular markers.

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Area of Science:

Biomedical data analysis
Computational biology
Translational medicine

Background:

High-throughput experimental methods generate vast molecular datasets.
Correlational studies link molecular measurements to diseases like Alzheimer's, liver, and gastric cancer.
Selecting a limited set of biomarkers is crucial for practical clinical applications, avoiding spurious correlations.

Purpose of the Study:

To evaluate 4 biomarker selection methods and 5 machine learning (ML) classifiers.
To compare 20 different approaches for identifying disease-associated biomarkers.
To assess the performance of contemporary methods against traditional logistic regression.

Main Methods:

Utilized 4 distinct biomarker selection techniques.
Employed 5 different machine learning classifiers.
Evaluated 20 unique biomarker identification and classification approaches.

Main Results:

Contemporary methods significantly outperformed logistic regression when using 3 or 10 biomarkers.
With 0.9 specificity, ML classifiers achieved 0.240 sensitivity (3 biomarkers) and 0.520 sensitivity (10 biomarkers).
Causal-based selection excelled with fewer biomarkers, while univariate selection performed best with more biomarkers.

Conclusions:

Advanced machine learning and causal-based biomarker selection methods offer superior diagnostic potential.
Optimizing biomarker selection strategies is critical for accurate and practical disease diagnosis.
This research provides a framework for selecting effective biomarkers in complex disease studies.