Linked Life Data

15514000

Source:http://linkedlifedata.com/resource/pubmed/id/15514000

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
7
pubmed:dateCreated
2005-3-24
pubmed:abstractText
The classification of samples using gene expression profiles is an important application in areas such as cancer research and environmental health studies. However, the classification is usually based on a small number of samples, and each sample is a long vector of thousands of gene expression levels. An important issue in parametric modeling for so many gene expression levels is the control of the number of nuisance parameters in the model. Large models often lead to intensive or even intractable computation, while small models may be inadequate for complex data.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
1367-4803
pubmed:author
pubmed:issnType
Print
pubmed:day
1
pubmed:volume
21
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1055-61
pubmed:dateRevised
2010-3-24
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
A novel means of using gene clusters in a two-step empirical Bayes method for predicting classes of samples.
pubmed:affiliation
Department of Biostatistics and Applied Mathematics, The University of Texas M.D. Anderson Cancer Center Houston, TX 77030, USA. yuanji@mdanderson.org
pubmed:publicationType
Journal Article, Comparative Study, Research Support, U.S. Gov't, P.H.S., Research Support, Non-U.S. Gov't, Evaluation Studies, Research Support, N.I.H., Extramural