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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Published on: October 11, 2018

Random forest for gene selection and microarray data classification.

Kohbalan Moorthy1, Mohd Saberi Mohamad

  • 1Artificial Intelligence & Bioinformatics Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia.

Bioinformation
|November 30, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced random forest method for gene selection and microarray data classification. The improved technique efficiently identifies informative gene subsets, leading to higher classification accuracy and lower prediction errors.

Keywords:
Random forestcancer classificationclassificationgene expression datagene selectionmicroarray data

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Area of Science:

  • Bioinformatics
  • Machine Learning
  • Genomics

Background:

  • Microarray data analysis requires effective gene selection for accurate classification.
  • Existing methods may not optimally balance gene subset size and classification accuracy.

Purpose of the Study:

  • To propose an enhanced random forest method for simultaneous gene selection and classification of microarray data.
  • To improve classification accuracy by optimizing gene subset selection.

Main Methods:

  • An embedded gene selection approach using a random forest algorithm was employed.
  • The method was enhanced to select both the smallest and largest informative gene subsets.
  • Classification was performed using the selected gene subsets.

Main Results:

  • The enhanced random forest method identified smaller and larger informative gene subsets with lower out-of-bag error rates.
  • Classification using the selected gene subsets resulted in reduced prediction error rates compared to existing methods.

Conclusions:

  • The enhanced random forest gene selection method improves both the identification of informative gene subsets and classification accuracy.
  • This approach offers a valuable tool for researchers analyzing microarray data and seeking informative genes for further study.