Linked Life Data

18478083

Source:http://linkedlifedata.com/resource/pubmed/id/18478083

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
7
pubmed:dateCreated
2008-5-14
pubmed:abstractText
Gene selection is to detect the most significantly expressed genes under different conditions expression data. The current challenge in gene selection is the comparison of a large number of genes with limited patient samples. Thus it is trivial task in simple statistical analysis. Various statistical measurements are adopted by filter methods applied in gene selection studies. Their ability to discriminate phenotypes is crucial in classification and selection. Here we describe the standard deviation error distribution (SDED) method for gene selection. It utilizes variations within-class and among-class in gene expression data. We tested the method using 4 leukemia datasets available in the public domain. The method was compared with the GS2 and CHO methods. The Prediction accuracies by SDED are better than both GS2 and CHO for different datasets. These are 0.8-4.2% and 1.6-8.4% more that in GS2 and CHO. The related OMIM annotations and KEGG pathways analyses verified that SDED can pick out more 4.0% and 6.1% genes with biological significance than GS2 and CHO, respectively.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:status
PubMed-not-MEDLINE
pubmed:issn
0973-2063
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
2
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
301-3
pubmed:year
2008
pubmed:articleTitle
SDED: a novel filter method for cancer-related gene selection.
pubmed:affiliation
Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China.
pubmed:publicationType
Journal Article