Linked Life Data

17607306

Source:http://linkedlifedata.com/resource/pubmed/id/17607306

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
8
pubmed:dateCreated
2007-7-19
pubmed:abstractText
Many examples highlight the power of gene expression profiles, or signatures, to inform an understanding of biological phenotypes. This is perhaps best seen in the context of cancer, where expression signatures have tremendous power to identify new subtypes and to predict clinical outcomes. Although the ability to interpret the meaning of the individual genes in these signatures remains a challenge, this does not diminish the power of the signature to characterize biological states. The use of these signatures as surrogate phenotypes has been particularly important, linking diverse experimental systems that dissect the complexity of biological systems with the in vivo setting in a way that was not previously feasible.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1471-0056
pubmed:author
pubmed:issnType
Print
pubmed:volume
8
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
601-9
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Mining gene expression profiles: expression signatures as cancer phenotypes.
pubmed:affiliation
Duke Institute for Genome Sciences and Policy, Duke University Medical Center, Durham, North Carolina 27710, USA. nevin001@mc.duke.edu
pubmed:publicationType
Journal Article, Review