Linked Life Data

16369572

Source:http://linkedlifedata.com/resource/pubmed/id/16369572

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2005-12-21
pubmed:abstractText
In just a few years, microarrays have gone from obscurity to being almost ubiquitous in biological research. At the same time, the statistical methodology for microarray analysis has progressed from simple visual assessments of results to a weekly deluge of papers that describe purportedly novel algorithms for analysing changes in gene expression. Although the many procedures that are available might be bewildering to biologists who wish to apply them, statistical geneticists are recognizing commonalities among the different methods. Many are special cases of more general models, and points of consensus are emerging about the general approaches that warrant use and elaboration.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
1471-0056
pubmed:author
pubmed:issnType
Print
pubmed:volume
7
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
55-65
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Microarray data analysis: from disarray to consolidation and consensus.
pubmed:affiliation
Section on Statistical Genetics, Department of Biostatistics, Ryals Public Health Building, 1665 University Avenue, University of Alabama at Birmingham, Alabama 35294-0022, USA. Dallison@uab.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Review, Research Support, N.I.H., Extramural