Linked Life Data

12529876

Source:http://linkedlifedata.com/resource/pubmed/id/12529876

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2003-1-16
pubmed:abstractText
Rapid advances in biotechnology have resulted in an increasing interest in the use of oligonucleotide and spotted cDNA gene expression microarrays for medical research. These arrays are being widely used to understand the underlying genetic structure of various diseases, with the ultimate goal to provide better diagnosis, prevention and cure. This technology allows for measurement of expression levels from several thousands of genes simultaneously, thus resulting in an enormous amount of data. The role of the statistician is critical to the successful design of gene expression studies, and the analysis and interpretation of the resulting voluminous data. This paper discusses hypotheses common to gene expression studies, and describes some of the statistical methods suitable for addressing these hypotheses. S-plus and SAS codes to perform the statistical methods are provided. Gene expression data from an unpublished oncologic study is used to illustrate these methods.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
0277-6715
pubmed:author
pubmed:copyrightInfo
Copyright 2003 John Wiley & Sons, Ltd.
pubmed:issnType
Print
pubmed:day
15
pubmed:volume
22
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
481-99
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
2003
pubmed:articleTitle
A statistical perspective on gene expression data analysis.
pubmed:affiliation
Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10021, USA. satago@biosta.mskcc.org
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Review