Gene selection algorithm by combining reliefF and mRMR

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 b...

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Veröffentlicht in:BMC genomics Jg. 9; H. Suppl 2; S. S27
Hauptverfasser: Zhang, Yi, Ding, Chris, Li, Tao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 16.09.2008
BMC
Schlagworte:
Algorithms
Animal Genetics and Genomics
Artificial Intelligence
Biomedical and Life Sciences
Gene Expression
Gene Expression Profiling - methods
Humans
Life Sciences
Microarrays
Microbial Genetics and Genomics
Models, Statistical
Neoplasms - genetics
Oligonucleotide Array Sequence Analysis - methods
Pattern Recognition, Automated - methods
Plant Genetics and Genomics
Proteomics
Software
Feature Selection
Support Vector Machine
Feature Selection Method
Gene Selection
Selection Algorithm
ISSN:1471-2164, 1471-2164
Online-Zugang:Volltext
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Zusammenfassung: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.
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ISSN:1471-2164
1471-2164
DOI:10.1186/1471-2164-9-S2-S27