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Related Experiment Video

Updated: May 20, 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

Thresholding for biomarker selection in multivariate data using Higher Criticism.

Ron Wehrens¹, Pietro Franceschi

¹Centre for Research and Innovation, Fondazione Edmund Mach., Via E. Mach 1, San Michele all'Adige (TN), Italy. ron.wehrens@fmach.it

Molecular Biosystems

|June 30, 2012

Summary

This summary is machine-generated.

This study introduces an improved method for biomarker selection in omics sciences. The approach uses Higher Criticism to find data-specific cutoffs, enhancing the identification of true biological signals.

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Area of Science:

Omics sciences
Biostatistics
Bioinformatics

Background:

Biomarker selection is crucial in omics studies for understanding biological processes.
Identifying true biomarkers from numerous variables is challenging.
Existing methods often struggle with determining appropriate cutoff values.

Purpose of the Study:

To extend the Higher Criticism (HC) method for multivariate biomarker selection.
To develop a principled approach for balancing true positive detection with minimizing false positives.
To provide data-specific cutoff values for more reliable biomarker identification.

Main Methods:

Application of Higher Criticism (HC) to multivariate data.
Development of data-dependent cutoff selection for biomarker identification.
Comparison of HC approach with standard methods for biomarker selection.

Main Results:

The extended HC method shows marked improvement in biomarker selection.
HC-derived thresholds differ significantly from previously suggested values.
Data-specific cutoffs enable fairer comparisons between different biomarker selection methods.

Conclusions:

Higher Criticism offers a robust approach for multivariate biomarker selection in omics data.
The use of data-specific thresholds is essential for accurate biomarker discovery.
This method improves the balance between sensitivity and specificity in biomarker identification.