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Related Experiment Video

Updated: Jun 23, 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

Multiclass cancer classification by support vector machines with class-wise optimized genes and probability

Ashish Anand1, P N Suganthan

  • 1School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, S2-B2a-21, Singapore 639798, Singapore.

Journal of Theoretical Biology
|May 2, 2009
PubMed
Summary

This study introduces a novel framework for multiclass cancer classification using optimized genes and one-versus-all support vector machine (OVA-SVM) classifiers. Ensemble methods improved accuracy, especially for unbalanced cancer datasets.

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Last Updated: Jun 23, 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:

  • Multiclass cancer classification from gene expression data is challenging and less explored than binary classification.
  • Accurate subtyping of cancer is crucial for effective treatment strategies.

Purpose of the Study:

  • To develop and validate a robust framework for multiclass cancer classification using microarray data.
  • To evaluate the efficacy of class-wise optimized gene selection and one-versus-all support vector machine (OVA-SVM) classifiers.
  • To assess the impact of different probability estimation methods and ensemble techniques on classification accuracy.

Main Methods:

  • Implemented class-wise gene selection coupled with one-versus-all support vector machine (OVA-SVM) classifiers.
  • Utilized three distinct probability estimation methods (including Platt's and isotonic approaches) from classifier decision values.
  • Conducted ensemble experiments to enhance predictive accuracy and performed four-fold external stratified cross-validation on six multiclass cancer datasets.

Main Results:

  • The class-wise gene selection approach identified relevant genes consistent with existing literature.
  • Platt's probability method showed consistency, while the isotonic approach performed better on imbalanced datasets.
  • Ensemble methods significantly improved classification accuracy, particularly for datasets with unequal sample proportions.

Conclusions:

  • The proposed framework effectively performs multiclass cancer classification and aids in identifying biologically relevant genes.
  • Probability-based comparisons and ensemble strategies enhance the reliability and accuracy of cancer subtype prediction.
  • This approach offers a valuable tool for analyzing complex cancer microarray data and advancing personalized medicine.