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Updated: Jan 10, 2026

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

A stable feature selection method based on majority voting and SHAP for high-dimensional metabolomics data.

Zixuan Liu¹, Jianqiang Du², Jigen Luo³

¹School of Intelligent Medicine and Information Engineering, Jiangxi University of Chinese Medicine, Nanchang 330004, China.

Computer Methods and Programs in Biomedicine

|November 25, 2025

Summary

This summary is machine-generated.

This study introduces a novel feature selection framework, MVFS-SHAP, to improve stability and accuracy in metabolomics data analysis. The method enhances biomarker discovery for complex diseases.

Keywords:

Feature selection High-dimensional small samples Metabolomics SHAP Stability

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Last Updated: Jan 10, 2026

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

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

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Published on: November 29, 2024

Area of Science:

Metabolomics
Bioinformatics
Computational Biology

Background:

Metabolomics enables large-scale metabolite measurement for disease research.
High-dimensional, small-sample metabolomics data requires robust feature selection.
Current methods lack stability, impacting feature consistency.

Purpose of the Study:

To develop a stable and accurate feature selection framework for metabolomics.
To enhance the reliability of biomarker screening in complex diseases.

Main Methods:

Proposed MVFS-SHAP framework integrating majority voting and SHAP values.
Utilized cross-validation and bootstrap sampling for robust feature subset generation.
Employed Ridge regression and Linear SHAP for feature re-ranking and selection.
Evaluated stability using the extended Kuncheva index and predictive performance via PLS regression.

Main Results:

MVFS-SHAP demonstrated superior stability and predictive accuracy over existing methods.
Achieved high stability (0.90+) on Exo and Endo datasets, with 80% of results >0.80.
Maintained stability (0.50-0.75) even on challenging datasets.
Reduced RMSE values across various predictive models (Lasso, Random Forest, XGBoost).

Conclusions:

MVFS-SHAP offers a stable and effective solution for feature selection in metabolomics.
Framework shows robustness in handling noisy, complex data for reliable biomarker selection.
Future work includes enhancing adaptability and applying to precision medicine and TCM research.