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Related Experiment Video

Updated: May 12, 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

An Ensemble Classifier for Ordinal Outcomes in High-Dimensional Genomics Data.

Heranga K Rathnasekara¹, Sinjini Sikdar¹

¹Department of Mathematics and Statistics, Old Dominion University, Norfolk, Virginia, USA.

Pharmaceutical Statistics

|May 10, 2026

Summary

This summary is machine-generated.

This study introduces an ensemble ordinal classifier for predicting disease outcomes from genomic data. The novel method improves prediction accuracy and robustness by combining multiple models, outperforming single approaches.

Keywords:

bagging classification ensemble genomic data high‐dimensional ordinal

Related Experiment Videos

Last Updated: May 12, 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

Area of Science:

Genomics and Bioinformatics
Computational Biology
Statistical Genetics

Background:

Predicting disease outcomes using genomics data is crucial in medical research.
Ordinal classification is essential for categorical outcomes in high-dimensional genomic datasets.
Existing high-dimensional ordinal models show variable performance.

Purpose of the Study:

To develop an ensemble ordinal classifier for improved genomic data analysis.
To enhance the robustness and predictive accuracy of disease outcome prediction.
To address the limitations of single high-dimensional ordinal models.

Main Methods:

An ensemble ordinal classifier integrating diverse ordinal modeling approaches.
Bootstrap-based model evaluation for robust assessment.
Multi-metric performance assessment and rank aggregation for final prediction.

Main Results:

The ensemble method consistently ranked among top-performing models in simulations and real data.
Demonstrated improved robustness and predictive accuracy compared to single models.
Successfully applied to real genomic data analyses.

Conclusions:

Ensemble learning offers a powerful strategy for high-dimensional ordinal classification in genomics.
The proposed ensemble classifier alleviates uncertainty associated with single model reliance.
This approach enhances the reliability of disease outcome prediction from genomic profiles.