15852500

Source:http://linkedlifedata.com/resource/pubmed/id/15852500

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Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C0017262, umls-concept:C0036576, umls-concept:C1313915, umls-concept:C1511726, umls-concept:C1524031, umls-concept:C1709016, umls-concept:C2348519
pubmed:issue	2
pubmed:dateCreated	2005-4-26
pubmed:abstractText	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/.
pubmed:grant	http://linkedlifedata.com/resource/pubmed/grant/R01 GM70444-01
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/101187344
pubmed:citationSubset	IM
pubmed:status	MEDLINE
pubmed:month	Apr
pubmed:issn	0219-7200
pubmed:author	pubmed-author:DingChrisC, pubmed-author:PengHanchuanH
pubmed:issnType	Print
pubmed:volume	3
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	185-205
pubmed:dateRevised	2007-11-14
pubmed:meshHeading	pubmed-meshheading:15852500-Algorithms, pubmed-meshheading:15852500-Discriminant Analysis, pubmed-meshheading:15852500-Gene Expression Profiling, pubmed-meshheading:15852500-Oligonucleotide Array Sequence Analysis, pubmed-meshheading:15852500-Pattern Recognition, Automated
pubmed:year	2005
pubmed:articleTitle	Minimum redundancy feature selection from microarray gene expression data.
pubmed:affiliation	Computational Research Division, Lawrence Berkeley National Laboratory, University of California, Berkeley, CA 94720, USA. chqding@lbl.gov
pubmed:publicationType	Journal Article, Comparative Study, Research Support, U.S. Gov't, P.H.S., Research Support, U.S. Gov't, Non-P.H.S., Evaluation Studies, Research Support, N.I.H., Extramural