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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

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Published on: October 11, 2018

Gene selection algorithm by combining reliefF and mRMR.

Yi Zhang1, Chris Ding, Tao Li

  • 1School of Computer Science, Florida International University, 11200 SW 8th Street, Miami, FL 33199, USA. yzhan004@cs.fiu.edu

BMC Genomics
|October 10, 2008
PubMed
Summary

This study introduces an effective two-stage gene selection algorithm, mRMR-ReliefF, for analyzing gene expression data. It efficiently identifies key genes to distinguish between biological sample types.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression datasets typically feature numerous genes and limited samples.
  • Effective feature selection is crucial for identifying genes that differentiate biological sample types.

Purpose of the Study:

  • To present a novel two-stage gene selection algorithm combining ReliefF and Minimum Redundancy Maximum Relevance (mRMR).
  • To enhance the identification of biologically relevant gene subsets from high-dimensional data.

Main Methods:

  • A two-stage approach integrating ReliefF for initial candidate gene identification.
  • Subsequent application of mRMR to reduce redundancy and select a compact gene subset.

Main Results:

  • The mRMR-ReliefF algorithm demonstrated high effectiveness in comparative experiments.
  • Performance was evaluated against other methods using Support Vector Machines (SVM) and Naive Bayes classifiers across seven datasets.

Conclusions:

  • The mRMR-ReliefF algorithm is a highly effective method for gene selection in expression data analysis.
  • The study provides source codes and datasets for reproducibility and further research.