Linked Life Data

15474670

Source:http://linkedlifedata.com/resource/pubmed/id/15474670

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2004-10-11
pubmed:abstractText
Prognosis following a diagnosis of primary lung cancer is very poor and varies significantly even after adjusting for known predictors. Inherent and acquired gene alterations could cause failure in lung cancer treatment and patient survival. To search for potential molecular markers with significant and independent predictive value in lung cancer survival, we applied oligo-nucleotide microarray analysis, along with patients' phenotypic profile, in a case-control study. The focus of this report is on the methodology used in the identification of potential genes as prognostic factors.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
0169-5002
pubmed:author
pubmed:issnType
Print
pubmed:volume
46
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
215-26
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Study design considerations in clinical outcome research of lung cancer using microarray analysis.
pubmed:affiliation
Division of Epidemiology, Mayo Clinic, Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA. yang.ping@mayo.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, Non-U.S. Gov't