2005/04/01 by Chris Ding, CHRIS DING, HANCHUAN PENG · 1 citation
Biochemistry, Genetics and Molecular Biology · #Artificial intelligence #Bayes' theorem #Bayesian probability #Bioinformatics and Genomic Networks #Biology #Computational biology #Computer science #DNA microarray #Data mining #Feature selection #Gene #Gene expression #Gene expression and cancer classification #Genetics #Machine Learning in Bioinformatics #Microarray analysis techniques #Minimum redundancy feature selection #Naive Bayes classifier #Pattern recognition (psychology) #Phenotype #Redundancy (engineering) #Support vector machine
paper · doi:10.1142/s0219720005001004
crossref issued 2005/04/01 · crossref published 2005/04/01 · crossref published-print 2005/04/01 · openalex publication_date 2005/04/01 · crossref created 2005/04/15 · crossref published-online 2011/11/21 · crossref deposited 2019/08/07 · openalex created_date 2025/10/10 · crossref indexed 2026/08/03 · openalex updated_date 2026/08/04
How to selecting a small subset out of the thousands of genes in microarray data is important for accurate classification of phenotypes. Widely used methods typically rank genes according to their differential expressions among phenotypes and pick the top-ranked genes. We observe that feature sets so obtained have certain redundancy and study methods to minimize it. We propose a minimum redundancy - maximum relevance (MRMR) feature selection framework. Genes selected via MRMR provide a more balanced coverage of the space and capture broader characteristics of phenotypes. They lead to significantly improved class predictions in extensive experiments on 6 gene expression data sets: NCI, Lymphoma, Lung, Child Leukemia, Leukemia, and Colon. Improvements are observed consistently among 4 classification methods: Naive Bayes, Linear discriminant analysis, Logistic regression, and Support vector machines. SUPPLIMENTARY: The top 60 MRMR genes for each of the datasets are listed in http://crd.lbl.gov/~cding/MRMR/. More information related to MRMR methods can be found at http://www.hpeng.net/.