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Updated: Jan 8, 2026

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

Benchmarking single-sample gene set scoring methods for application in precision medicine.

Daniel Toro-Domínguez^1,2, Chang Wang², Iván Ellson-Lancho³

¹Unit of Inflammatory Diseases, Department of Environmental Medicine, Karolinska Institute, Nobel väg 13, Solna 171 67, Sweden.

Briefings in Bioinformatics

|December 17, 2025

Summary

This summary is machine-generated.

Gene set-based single-sample scoring methods aid precision medicine by revealing disease heterogeneity. This study systematically evaluated their performance, offering crucial insights for selecting appropriate methods.

Keywords:

data integration gene set scoring patient stratification precision medicine predictive modeling single-sample transcriptomics

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Single-sample scoring methods are vital for interpreting molecular data in precision medicine.
Existing algorithms lack systematic performance evaluation across diverse scenarios.

Purpose of the Study:

To comprehensively survey and evaluate single-sample scoring methods.
To assess method performance regarding stability, reproducibility, and downstream applications.

Main Methods:

Conducted a systematic survey of numerous single-sample scoring algorithms.
Evaluated performance under data limitations and integration scenarios.
Assessed downstream utility in patient stratification, clinical association, and predictive modeling.

Main Results:

Identified variations in stability and reproducibility across methods.
Demonstrated differential performance in downstream precision medicine tasks.
Highlighted the impact of data quality and integration on method outcomes.

Conclusions:

Method selection critically impacts precision medicine outcomes.
Rational design of analysis strategies is essential for reliable results.
This study provides foundational insights for choosing appropriate scoring methods.