| pubmed-article:19759197 | rdf:type | pubmed:Citation | lld:pubmed |
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| pubmed-article:19759197 | lifeskim:mentions | umls-concept:C0017428 | lld:lifeskim |
| pubmed-article:19759197 | lifeskim:mentions | umls-concept:C0439064 | lld:lifeskim |
| pubmed-article:19759197 | lifeskim:mentions | umls-concept:C0009085 | lld:lifeskim |
| pubmed-article:19759197 | lifeskim:mentions | umls-concept:C0185125 | lld:lifeskim |
| pubmed-article:19759197 | lifeskim:mentions | umls-concept:C0681946 | lld:lifeskim |
| pubmed-article:19759197 | lifeskim:mentions | umls-concept:C0936012 | lld:lifeskim |
| pubmed-article:19759197 | lifeskim:mentions | umls-concept:C0449560 | lld:lifeskim |
| pubmed-article:19759197 | lifeskim:mentions | umls-concept:C1609081 | lld:lifeskim |
| pubmed-article:19759197 | pubmed:issue | 22 | lld:pubmed |
| pubmed-article:19759197 | pubmed:dateCreated | 2009-11-5 | lld:pubmed |
| pubmed-article:19759197 | 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. | lld:pubmed |
| pubmed-article:19759197 | pubmed:grant | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:19759197 | pubmed:commentsCorrections | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:19759197 | pubmed:language | eng | lld:pubmed |
| pubmed-article:19759197 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:19759197 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:19759197 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:19759197 | pubmed:month | Nov | lld:pubmed |
| pubmed-article:19759197 | pubmed:issn | 1367-4811 | lld:pubmed |
| pubmed-article:19759197 | pubmed:author | pubmed-author:LadanyiMarcM | lld:pubmed |
| pubmed-article:19759197 | pubmed:author | pubmed-author:OlshenAdam... | lld:pubmed |
| pubmed-article:19759197 | pubmed:author | pubmed-author:ShenRonglaiR | lld:pubmed |
| pubmed-article:19759197 | pubmed:issnType | Electronic | lld:pubmed |
| pubmed-article:19759197 | pubmed:day | 15 | lld:pubmed |
| pubmed-article:19759197 | pubmed:volume | 25 | lld:pubmed |
| pubmed-article:19759197 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:19759197 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:19759197 | pubmed:pagination | 2906-12 | lld:pubmed |
| pubmed-article:19759197 | pubmed:dateRevised | 2010-11-22 | lld:pubmed |
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| pubmed-article:19759197 | pubmed:meshHeading | pubmed-meshheading:19759197... | lld:pubmed |
| pubmed-article:19759197 | pubmed:year | 2009 | lld:pubmed |
| pubmed-article:19759197 | pubmed:articleTitle | Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. | lld:pubmed |
| pubmed-article:19759197 | pubmed:affiliation | Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA. shenr@mskcc.org | lld:pubmed |
| pubmed-article:19759197 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:19759197 | pubmed:publicationType | Research Support, N.I.H., Extramural | lld:pubmed |
| http://linkedlifedata.com/r... | pubmed:referesTo | pubmed-article:19759197 | lld:pubmed |
