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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Gradient boosting with knockoff filters: a biostatistical approach to variable selection.

Amr Mohamed¹, Kevin H Lee²

¹College of Health Sciences, The University of Memphis, 3720 Alumni Ave, Memphis, TN, 38152, USA. A.Mohamed@memphis.edu.

BMC Bioinformatics

|November 26, 2025

Summary

This study introduces a novel variable selection method integrating knockoffs with Light Gradient Boosting Machine (LightGBM) and SHAP values. The approach efficiently identifies significant variables while controlling False Discovery Rate (FDR), outperforming traditional methods in big data scenarios.

Keywords:

Gradient boosting Knockoffs LightGBM SHAP Variable selection

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Area of Science:

Statistics
Machine Learning
Data Science

Background:

Increasing data complexity necessitates efficient variable selection methods.
Controlling False Discovery Rate (FDR) and maintaining statistical power are key challenges.
Knockoff filters offer a robust approach by creating negative controls for inference.

Purpose of the Study:

To extend the application of knockoff filters to Light Gradient Boosting Machine (LightGBM).
To enhance variable selection accuracy and efficiency in the context of big data.
To improve the interpretability of machine learning models using SHAP values.

Main Methods:

Integration of knockoff variable generation with LightGBM.
Utilization of Shapely Additive Explanations (SHAP) for model interpretability.
Extensive experimentation and simulation studies for validation.

Main Results:

The proposed method accurately identifies important variables for each class.
Demonstrated superior performance, speed, and efficiency compared to traditional methods.
Enhanced interpretability of variable importance through SHAP values.

Conclusions:

The integration of knockoffs into LightGBM provides a powerful tool for variable selection.
This approach effectively addresses challenges in big data analysis.
The method advances statistical modeling and machine learning applications by improving performance and interpretability.