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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Comparison of feature selection and classification combinations for cancer classification using microarray data.

Vijayan Vinaya1, Nadeem Bulsara, Chetan J Gadgil

  • 1Department of Bioinformatics, Dr. D.Y. Patil Biotechnology and Bioinformatics Institute, Akurdi, Pune 411044, India. vini_vij86@yahoo.co.in

International Journal of Bioinformatics Research and Applications
|July 31, 2009
PubMed
Summary

This study identifies optimal gene selection and classification algorithms for accurate cancer diagnosis using high throughput gene expression data. A combination of Support Vector Machine (SVM) and Sequential Minimal Optimization (SMO) achieved 96% accuracy, even on independent data.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High throughput gene expression data aids in identifying biomarker profiles for sample classification.
  • Microarray-based classification accuracy is influenced by both feature (gene) selection and classification algorithms.

Purpose of the Study:

  • To evaluate over 2000 combinations of feature selection and classification algorithms for cancer datasets.
  • To identify the most effective algorithm combination for accurate cancer classification.

Main Methods:

  • Systematic evaluation of more than 2000 feature selection and classification algorithm combinations.
  • Utilized cancer datasets for performance assessment.
  • Focused on Support Vector Machine (SVM) for gene ranking and Sequential Minimal Optimization (SMO) for classification.

Main Results:

  • One combination (SVM for ranking genes + SMO) demonstrated excellent classification accuracy.
  • Achieved high accuracy using a minimal set of selected genes across three cancer datasets.
  • Attained 96% accuracy for classification on an independent microarray platform using only 15 genes.

Conclusions:

  • The SVM + SMO algorithm combination is highly effective for gene expression-based cancer classification.
  • This approach enables accurate cancer diagnosis with a small number of biomarkers.
  • The findings have implications for developing robust diagnostic tools in oncology.