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Related Experiment Video

Updated: Jun 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

Robust model selection for classification of microarrays.

Ikumi Suzuki¹, Takashi Takenouchi, Miki Ohira

¹Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, Nara, Japan.

Cancer Informatics

|September 1, 2009

Summary

This summary is machine-generated.

This study introduces a new min-max criterion for selecting reliable gene expression classifiers in cancer diagnosis. It reduces the risk of choosing poor classifiers, improving diagnostic system safety.

Keywords:

cancer diagnosis gene expression mini-chip microarrays supervised analysis

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Microarray-based cancer diagnosis systems are under investigation.
Cost reduction and reliability are key challenges for clinical application.
Supervised classifiers require minimal genes for cost-effectiveness while maintaining reliability.

Purpose of the Study:

To develop a robust model selection criterion for reliable cancer diagnosis.
To address the variance issue in classifier assessment due to limited samples and noise.
To propose a method that mitigates the risk of selecting poor classifiers.

Main Methods:

Proposed a novel min-max criterion based on resampling bootstrap simulation.
Assessed the variance of classification error rate estimations.
Applied the framework to real and synthetic gene expression datasets.

Main Results:

Identified a non-negligible risk associated with state-of-the-art weighted voting classifiers using the leave-one-out (LOO) criterion.
Demonstrated that the min-max criterion effectively eliminates the risk of selecting poor classifiers.
Validated findings on four real and one synthetic gene expression datasets.

Conclusions:

The proposed min-max criterion offers a safer procedure for designing practical cancer diagnosis systems.
This approach enhances the reliability of gene selection for microarray-based diagnostics.
Minimizes the selection of unreliable classifiers, crucial for clinical settings.