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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Published on: October 11, 2018

Incorporating feature ranking and evolutionary methods for the classification of high-dimensional DNA microarray gene

Mani Abedini1, Michael Kirley, Raymond Chiong

  • 1Department of Computing and Information Systems, The University of Melbourne, Victoria 3010, Australia ; IBM Research Australia, Carlton, Victoria 3053, Australia.

The Australasian Medical Journal
|June 8, 2013
PubMed
Summary

Feature ranking improves microarray gene expression classification more than feature reduction. Using feature quality information to guide learning enhances accuracy without limiting exploration, offering a superior approach.

Keywords:
ClassificationGRD-XCSXCSeXtended Classifier Systemevolutionary algorithmsfeature rankingguided rule discovery XCShigh-dimensional datamicroarray gene expression profiling

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Area of Science:

  • Bioinformatics
  • Machine Learning
  • Computational Biology

Background:

  • DNA microarray gene expression data presents high dimensionality challenges.
  • Feature selection is a common approach to reduce dimensionality in machine learning tasks.
  • Accurate classification of gene expression data is crucial for biological insights.

Purpose of the Study:

  • To investigate the impact of feature quality information on microarray gene expression classification precision.
  • To compare feature reduction versus feature ranking for improving classification performance.
  • To develop enhanced machine learning models for high-dimensional biological data.

Main Methods:

  • Proposed two evolutionary machine learning models: FS-XCS (feature selection) and GRD-XCS (feature ranking).
  • Utilized the eXtended Classifier System (XCS) framework.
  • Applied feature selection and ranking methodologies to gene expression datasets.

Main Results:

  • Feature selection/ranking is essential for high-dimensional classification tasks like microarray analysis.
  • Biasing rule discovery with feature ranking significantly outperformed feature reduction.
  • Leveraging feature quality for smarter learning procedures proved more efficient than simple reduction.

Conclusions:

  • Extracting feature quality information aids the learning process and boosts classification accuracy.
  • Exclusive reliance on feature reduction may decrease classification performance.
  • A hybrid approach, using feature quality to direct learning while allowing exploration, is recommended.