Linked Life Data

19759197

Source:http://linkedlifedata.com/resource/pubmed/id/19759197

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
22
pubmed:dateCreated
2009-11-5
pubmed:abstractText
The molecular complexity of a tumor manifests itself at the genomic, epigenomic, transcriptomic and proteomic levels. Genomic profiling at these multiple levels should allow an integrated characterization of tumor etiology. However, there is a shortage of effective statistical and bioinformatic tools for truly integrative data analysis. The standard approach to integrative clustering is separate clustering followed by manual integration. A more statistically powerful approach would incorporate all data types simultaneously and generate a single integrated cluster assignment.
pubmed:grant
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
1367-4811
pubmed:author
pubmed:issnType
Electronic
pubmed:day
15
pubmed:volume
25
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2906-12
pubmed:dateRevised
2010-11-22
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis.
pubmed:affiliation
Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA. shenr@mskcc.org
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural