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Related Experiment Videos

Structural Risk Minimisation based gene expression profiling analysis.

Xue-Wen Chen1, Byron Gerlach, Dechang Chen

  • 1Bioinformatics and Computational Life Sciences Laboratory, Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA. xwchen@ku.edu

International Journal of Bioinformatics Research and Applications
|December 1, 2007
PubMed
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Structural Risk Minimisation (SRM) improves gene selection for cancer classification by preventing overfitting common in small sample datasets. This method offers better performance than traditional cross-validation approaches.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Cancer Research

Background:

  • Microarray-based cancer classification relies on feature selection to enhance classifier generalization.
  • Wrapper methods commonly use cross-validation, which can overfit small sample datasets like microarrays.

Purpose of the Study:

  • To propose a Structural Risk Minimisation (SRM) based method for gene selection in cancer classification.
  • To address overfitting issues inherent in cross-validation for small sample problems.

Main Methods:

  • Implementation of a Structural Risk Minimisation (SRM) principle for gene selection.
  • Evaluation of the proposed SRM-based method against traditional wrapper methods using cross-validation.

Main Results:

Related Experiment Videos

  • The proposed SRM-based gene selection method significantly reduces generalization error bounds.
  • Demonstrated superior performance compared to conventional wrapper methods that employ cross-validation.

Conclusions:

  • Structural Risk Minimisation offers an effective approach to gene selection for cancer classification.
  • The SRM method mitigates overfitting, leading to improved classifier performance on microarray data.