Gene selection algorithm by combining reliefF and mRMR

• Published in: BMC genomics Vol. 9; no. Suppl 2; p. S27
• Main Authors: Zhang, Yi, Ding, Chris, Li, Tao
• Format: Journal Article
• Language: English
• Published: London BioMed Central 16.09.2008; BMC
• Subjects: 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
• DOI: 10.1186/1471-2164-9-S2-S27

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.