Linked Life Data

20591905

Source:http://linkedlifedata.com/resource/pubmed/id/20591905

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
17
pubmed:dateCreated
2010-8-18
pubmed:abstractText
One of the main goals of high-throughput gene-expression studies in cancer research is to identify prognostic gene signatures, which have the potential to predict the clinical outcome. It is common practice to investigate these questions using classification methods. However, standard methods merely rely on gene-expression data and assume the genes to be independent. Including pathway knowledge a priori into the classification process has recently been indicated as a promising way to increase classification accuracy as well as the interpretability and reproducibility of prognostic gene signatures.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
1367-4811
pubmed:author
pubmed:issnType
Electronic
pubmed:day
1
pubmed:volume
26
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2136-44
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Integration of pathway knowledge into a reweighted recursive feature elimination approach for risk stratification of cancer patients.
pubmed:affiliation
German Cancer Research Center, Cancer Genome Research, Im Neuenheimer Feld 280, 69120 Heidelberg. m.johannes@DKFZ-heidelberg.de
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't