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Simultaneous gene clustering and subset selection for sample classification via MDL.

Rebecka Jörnsten1, Bin Yu

  • 1Department of Statistics, Rutgers University, 501 Hill Center, Piscataway, NJ 08854, USA. rebecka@stat.rutgers.edu

Bioinformatics (Oxford, England)
|June 13, 2003
PubMed
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This study introduces a novel algorithm for simultaneous gene clustering and sample classification, improving data interpretability and classification accuracy. The method provides sparse, stable, and interpretable classification rules for gene expression data.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray technology enables simultaneous monitoring of thousands of genes.
  • Gene expression data analysis often separates gene clustering and sample classification.
  • This separation can obscure informative data structures.

Purpose of the Study:

  • To develop an algorithm for simultaneous gene clustering and subset selection for sample classification.
  • To introduce a novel model selection criterion based on the minimum description length (MDL) principle.
  • To generate sparse and interpretable classification rules.

Main Methods:

  • Developed a new MDL-based model selection criterion for both genes and sample class labels.
  • Implemented an algorithm for simultaneous gene clustering and subset selection.

Related Experiment Videos

  • Applied the algorithm to publicly available gene expression datasets.

    • Main Results:

    • Achieved sparse and interpretable classification models based on cluster centroids.
    • Obtained competitive test error rates compared to existing methods.
    • Demonstrated stability and consistency of cluster centroids across cross-validation samples.

    Conclusions:

    • The simultaneous approach effectively integrates gene clustering and sample classification.
    • The algorithm yields robust, interpretable, and accurate classification rules.
    • The method enhances the discovery of class-informative structures within gene expression data.