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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Published on: October 11, 2018

Noise incorporated subwindow permutation analysis for informative gene selection using support vector machines.

Qin Wang1, Hong-Dong Li, Qing-Song Xu

  • 1Research Center of Modernization of Traditional Chinese Medicines, College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China.

The Analyst
|February 16, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces Noise Incorporated Subwindow Permutation Analysis (NISPA) for selecting informative genes in tumor sample prediction. NISPA effectively reduces prediction errors in Support Vector Machine (SVM) models by distinguishing informative, uninformative, and interfering variables.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning in Oncology

Background:

  • Accurate prediction of clinical tumor samples relies on identifying informative genes.
  • Existing variable selection methods may lack the precision to differentiate between truly informative and noise variables.

Purpose of the Study:

  • To propose a novel variable selection method, Noise Incorporated Subwindow Permutation Analysis (NISPA).
  • To enhance the accuracy of Support Vector Machine (SVM) classifiers in clinical tumor sample prediction.

Main Methods:

  • NISPA incorporates a noise variable into sub-datasets to establish a reference distribution.
  • The Mann-Whitney U test assigns P values to variables based on their difference from noise.
  • Variables are ranked to identify a subset of informative genes for model building.

Main Results:

  • NISPA successfully distinguishes between informative, uninformative (noise), and interfering variables.
  • Application to two microarray datasets demonstrated significant reduction in SVM prediction errors.
  • The proposed method provides a more detailed classification of variable importance compared to existing approaches.

Conclusions:

  • NISPA is a robust and effective algorithm for variable selection in gene expression analysis.
  • This method offers a valuable alternative for improving the accuracy of predictive models in cancer research.