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Related Experiment Videos

Regularization strategies for hyperplane classifiers: application to cancer classification with gene expression data.

Erik Andries1, Thomas Hagstrom, Susan R Atlas

  • 1Department of Mathematics & Statistics and, Center for High Performance Computing, University of New Mexico, Albuquerque, NM 87131, USA. andriese@math.unm.edu

Journal of Bioinformatics and Computational Biology
|May 5, 2007
PubMed
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This study addresses ill-posed problems in cancer gene expression analysis using linear discrimination. A Singular Value Decomposition-based method improves classifier performance and parameter selection for noisy data.

Area of Science:

  • Computational biology
  • Bioinformatics
  • Genomics

Background:

  • Linear discrimination is often ill-posed when applied to high-dimensional data like gene expression.
  • Noise in biological data can lead to unstable classification models.

Purpose of the Study:

  • To analyze the ill-posed nature of linear discrimination for cancer gene expression data.
  • To develop a robust method for classifying cancer subtypes and predicting outcomes.

Main Methods:

  • Applied linear discrimination to cancer gene expression data.
  • Utilized Singular Value Decomposition (SVD) for filter factor representation.
  • Investigated hyperplane-based separation in gene expression data.

Main Results:

Related Experiment Videos

  • Identified numerical ill-posedness in gene expression data separation using SVD.
  • Demonstrated that SVD-based filter factors provide diagnostic insights.
  • Showed improved classifier performance through guided parameter selection.

Conclusions:

  • SVD-based filter factor representation is effective for understanding and managing ill-posedness in cancer gene expression analysis.
  • This approach enhances the reliability and accuracy of cancer classification models.