| pubmed-article:16369572 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:16369572 | lifeskim:mentions | umls-concept:C0376298 | lld:lifeskim |
| pubmed-article:16369572 | lifeskim:mentions | umls-concept:C0010992 | lld:lifeskim |
| pubmed-article:16369572 | lifeskim:mentions | umls-concept:C1709016 | lld:lifeskim |
| pubmed-article:16369572 | pubmed:issue | 1 | lld:pubmed |
| pubmed-article:16369572 | pubmed:dateCreated | 2005-12-21 | lld:pubmed |
| pubmed-article:16369572 | pubmed:abstractText | In just a few years, microarrays have gone from obscurity to being almost ubiquitous in biological research. At the same time, the statistical methodology for microarray analysis has progressed from simple visual assessments of results to a weekly deluge of papers that describe purportedly novel algorithms for analysing changes in gene expression. Although the many procedures that are available might be bewildering to biologists who wish to apply them, statistical geneticists are recognizing commonalities among the different methods. Many are special cases of more general models, and points of consensus are emerging about the general approaches that warrant use and elaboration. | lld:pubmed |
| pubmed-article:16369572 | pubmed:commentsCorrections | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:16369572 | pubmed:language | eng | lld:pubmed |
| pubmed-article:16369572 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:16369572 | pubmed:citationSubset | IM | lld:pubmed |
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| pubmed-article:16369572 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:16369572 | pubmed:month | Jan | lld:pubmed |
| pubmed-article:16369572 | pubmed:issn | 1471-0056 | lld:pubmed |
| pubmed-article:16369572 | pubmed:author | pubmed-author:AllisonDavid... | lld:pubmed |
| pubmed-article:16369572 | pubmed:author | pubmed-author:CuiXiangqinX | lld:pubmed |
| pubmed-article:16369572 | pubmed:author | pubmed-author:PageGrier PGP | lld:pubmed |
| pubmed-article:16369572 | pubmed:author | pubmed-author:SabripourMahy... | lld:pubmed |
| pubmed-article:16369572 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:16369572 | pubmed:volume | 7 | lld:pubmed |
| pubmed-article:16369572 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:16369572 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:16369572 | pubmed:pagination | 55-65 | lld:pubmed |
| pubmed-article:16369572 | pubmed:dateRevised | 2006-11-15 | lld:pubmed |
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| pubmed-article:16369572 | pubmed:year | 2006 | lld:pubmed |
| pubmed-article:16369572 | pubmed:articleTitle | Microarray data analysis: from disarray to consolidation and consensus. | lld:pubmed |
| pubmed-article:16369572 | pubmed:affiliation | Section on Statistical Genetics, Department of Biostatistics, Ryals Public Health Building, 1665 University Avenue, University of Alabama at Birmingham, Alabama 35294-0022, USA. Dallison@uab.edu | lld:pubmed |
| pubmed-article:16369572 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:16369572 | pubmed:publicationType | Research Support, U.S. Gov't, Non-P.H.S. | lld:pubmed |
| pubmed-article:16369572 | pubmed:publicationType | Review | lld:pubmed |
| pubmed-article:16369572 | pubmed:publicationType | Research Support, N.I.H., Extramural | lld:pubmed |
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