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Updated: Jul 17, 2026
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Effect of outlier removal on gene marker selection using support vector machines
Richard Moffitt1, John Phan, Scott Hemby
1Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University. 313 Ferst Drive, Atlanta, GA, USA 30332.
Summary
Outlier removal enhances biomarker discovery using Support Vector Machines (SVM). Preprocessing data with outlier removal improves the identification of predictive genes for disease diagnosis and prognosis.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Biological markers are crucial for disease diagnosis and prognosis.
- Gene expression microarrays are a common data source for marker identification.
- Support Vector Machines (SVM) are widely used for biomarker selection.
Purpose of the Study:
- To investigate the impact of outlier removal on SVM-based biomarker selection.
- To evaluate the effectiveness of outlier removal as a preprocessing step.
Main Methods:
- Employed a simple outlier removal technique before SVM analysis.
- Utilized both linear and radial basis kernels for SVM.
- Applied four different data normalization techniques.
Main Results:
- Outlier removal increased the number of highly predictive genes.
- Outlier removal also increased the number of poorly predicting genes.
- The preprocessing step generally improved SVM performance for biomarker identification.
Conclusions:
- Outlier removal is a beneficial preprocessing step for biological marker identification using SVM.
- This method aids in refining gene selection for diagnostic and prognostic applications.
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