| pubmed-article:16904203 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:16904203 | lifeskim:mentions | umls-concept:C0459471 | lld:lifeskim |
| pubmed-article:16904203 | lifeskim:mentions | umls-concept:C0425152 | lld:lifeskim |
| pubmed-article:16904203 | lifeskim:mentions | umls-concept:C1709016 | lld:lifeskim |
| pubmed-article:16904203 | lifeskim:mentions | umls-concept:C0870077 | lld:lifeskim |
| pubmed-article:16904203 | lifeskim:mentions | umls-concept:C0205460 | lld:lifeskim |
| pubmed-article:16904203 | pubmed:issue | 7 | lld:pubmed |
| pubmed-article:16904203 | pubmed:dateCreated | 2006-9-12 | lld:pubmed |
| pubmed-article:16904203 | pubmed:abstractText | Gene expression microarrays are now established as a standard tool in biological and biochemical laboratories. Interpreting the masses of data generated by this technology poses a number of unusual new challenges. Over the past few years a consensus has begun to emerge concerning the most important pitfalls and the proper ways to avoid them. This review provides an overview of these ideas, beginning with relevant aspects of experimental design and normalization, but focusing in particular on the various tools and concepts that help to interpret microarray results. These new approaches make it much easier to extract biologically relevant and reliable hypotheses in an objective and reasonably unbiased fashion. | lld:pubmed |
| pubmed-article:16904203 | pubmed:language | eng | lld:pubmed |
| pubmed-article:16904203 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:16904203 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:16904203 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:16904203 | pubmed:month | Jul | lld:pubmed |
| pubmed-article:16904203 | pubmed:issn | 0006-3002 | lld:pubmed |
| pubmed-article:16904203 | pubmed:author | pubmed-author:BreitlingRain... | lld:pubmed |
| pubmed-article:16904203 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:16904203 | pubmed:volume | 1759 | lld:pubmed |
| pubmed-article:16904203 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:16904203 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:16904203 | pubmed:pagination | 319-27 | lld:pubmed |
| pubmed-article:16904203 | pubmed:meshHeading | pubmed-meshheading:16904203... | lld:pubmed |
| pubmed-article:16904203 | pubmed:meshHeading | pubmed-meshheading:16904203... | lld:pubmed |
| pubmed-article:16904203 | pubmed:meshHeading | pubmed-meshheading:16904203... | lld:pubmed |
| pubmed-article:16904203 | pubmed:meshHeading | pubmed-meshheading:16904203... | lld:pubmed |
| pubmed-article:16904203 | pubmed:meshHeading | pubmed-meshheading:16904203... | lld:pubmed |
| pubmed-article:16904203 | pubmed:year | 2006 | lld:pubmed |
| pubmed-article:16904203 | pubmed:articleTitle | Biological microarray interpretation: the rules of engagement. | lld:pubmed |
| pubmed-article:16904203 | pubmed:affiliation | Groningen Bioinformatics Centre, University of Groningen, Kerklaan 30, 9751 NN Haren, The Netherlands. r.breitling@rug.nl | lld:pubmed |
| pubmed-article:16904203 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:16904203 | pubmed:publicationType | Review | lld:pubmed |
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| http://linkedlifedata.com/r... | pubmed:referesTo | pubmed-article:16904203 | lld:pubmed |
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