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This article is part of the supplement: IEEE 7th International COnference on Bioinformatics and Bioengineering at Harvard Medical School

Open Access Research

Gene selection algorithm by combining reliefF and mRMR

Yi Zhang1, Chris Ding2 and Tao Li1*

Author affiliations

1 School of Computer Science, Florida International University, 11200 SW 8th Street, Miami, FL, 33199, USA

2 Department of Computer Science and Engineering, University of Texas at Arlington, 416 Yates Street, Arlington, TX, 76019, USA

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Citation and License

BMC Genomics 2008, 9(Suppl 2):S27  doi:10.1186/1471-2164-9-S2-S27

Published: 16 September 2008

Abstract

Background

Gene expression data usually contains a large number of genes, but a small number of samples. Feature selection for gene expression data aims at finding a set of genes that best discriminate biological samples of different types. In this paper, we present a two-stage selection algorithm by combining ReliefF and mRMR: In the first stage, ReliefF is applied to find a candidate gene set; In the second stage, mRMR method is applied to directly and explicitly reduce redundancy for selecting a compact yet effective gene subset from the candidate set.

Results

We perform comprehensive experiments to compare the mRMR-ReliefF selection algorithm with ReliefF, mRMR and other feature selection methods using two classifiers as SVM and Naive Bayes, on seven different datasets. And we also provide all source codes and datasets for sharing with others.

Conclusion

The experimental results show that the mRMR-ReliefF gene selection algorithm is very effective.