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Related Experiment Video

Updated: Jun 13, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Comparison of two output-coding strategies for multi-class tumor classification using gene expression data and Latent

Sandeep J Joseph1, Kelly R Robbins, Wensheng Zhang

  • 1Rhodes Centre for Animal and Dairy Science, University of Georgia, Athens, GA 30605, USA.

Cancer Informatics
|May 12, 2010
PubMed
Summary
This summary is machine-generated.

This study compares One Versus One (OVO) and One Versus All (OVA) output coding for multi-class cancer classification using microarray data. OVO showed promise for brain cancer data, while both methods performed similarly on the Global Cancer Map dataset.

Keywords:
binary classifiergene expressiongibbs samplinglatent variable modelmulti-class tumor classificationsupervised classification

Related Experiment Videos

Last Updated: Jun 13, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Accurate multi-class cancer classification from microarray data is crucial for diagnosis and treatment.
  • Output coding strategies are essential for adapting binary classifiers to multi-class problems.

Purpose of the Study:

  • To compare the efficacy of One Versus One (OVO) and One Versus All (OVA) output coding strategies for multi-class cancer classification.
  • To evaluate the performance of these strategies on distinct microarray datasets (Global Cancer Map and brain cancer).

Main Methods:

  • Utilized a generalized output-coding scheme combining One Versus One (OVO) with Latent Variable Models (LVM).
  • Performed comparative analysis against the generalized One Versus All (OVA) method.
  • Employed fold change and penalized t-statistics for primary feature selection, with evaluation across varying feature numbers.

Main Results:

  • The OVO coding strategy demonstrated effectiveness with the brain cancer (BC) dataset.
  • Both OVO and OVA methods yielded comparable results for the Global Cancer Map (GCM) dataset.
  • Performance varied based on dataset characteristics, including the number of tumor types and data heterogeneity.

Conclusions:

  • The choice between OVO and OVA output coding for multi-class tumor classification is context-dependent.
  • Factors such as the number of tumor subtypes and training data characteristics influence the optimal strategy selection.