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Related Experiment Videos

Wrapper filtering criteria via linear neuron and kernel approaches.

Michalis E Blazadonakis1, Michalis Zervakis

  • 1Department of Electronic and Computer Engineering, Technical University of Crete, University Campus, Chania Crete 73100, Greece. mblazad@ier.forthnet.gr

Computers in Biology and Medicine
|July 29, 2008
PubMed
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Applying filter criteria recursively, akin to wrapper methods, significantly enhances DNA microarray marker selection performance. This novel approach improves generalization accuracy on independent datasets for applications like cancer research.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Marker selection is crucial for DNA microarray analysis.
  • Existing methods include filter (fixed weights) and wrapper (dynamic weights) approaches.

Purpose of the Study:

  • To investigate the recursive application of filter criteria for improved marker selection.
  • To demonstrate enhanced generalization performance using this novel approach.

Main Methods:

  • Exploration of Support Vector Machines (SVM) and a single Linear Neuron (LN) for marker selection.
  • Adaptation of pattern recognition techniques to recursive filter criteria application.
  • Utilizing SVM's kernel capabilities for practical marker selection.

Main Results:

Related Experiment Videos

  • Achieved comparable or superior results to existing methods in breast cancer and leukemia datasets.
  • Demonstrated stable performance irrespective of application domain complexity.
  • Validated using leave-one-out, 10-fold cross-validation, and independent test sets.

Conclusions:

  • Recursive application of filter criteria ('wrapper filtering criteria') is an effective marker selection tool.
  • Introduced a novel methodology combining filter and wrapper approaches.
  • Highlighted the utility of single neuron and SVM kernel methods in marker selection.