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| pubmed-article:12095422 | lifeskim:mentions | umls-concept:C0017262 | lld:lifeskim |
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| pubmed-article:12095422 | lifeskim:mentions | umls-concept:C1709016 | lld:lifeskim |
| pubmed-article:12095422 | lifeskim:mentions | umls-concept:C2349209 | lld:lifeskim |
| pubmed-article:12095422 | lifeskim:mentions | umls-concept:C1549649 | lld:lifeskim |
| pubmed-article:12095422 | lifeskim:mentions | umls-concept:C1880833 | lld:lifeskim |
| pubmed-article:12095422 | lifeskim:mentions | umls-concept:C0449445 | lld:lifeskim |
| pubmed-article:12095422 | pubmed:dateCreated | 2003-10-29 | lld:pubmed |
| pubmed-article:12095422 | pubmed:abstractText | The biomedical community is developing new methods of data analysis to more efficiently process the massive data sets produced by microarray experiments. Systematic and global mathematical approaches that can be readily applied to a large number of experimental designs become fundamental to correctly handle the otherwise overwhelming data sets. | lld:pubmed |
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| pubmed-article:12095422 | pubmed:language | eng | lld:pubmed |
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| pubmed-article:12095422 | pubmed:citationSubset | IM | lld:pubmed |
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| pubmed-article:12095422 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:12095422 | pubmed:month | Jun | lld:pubmed |
| pubmed-article:12095422 | pubmed:issn | 1471-2105 | lld:pubmed |
| pubmed-article:12095422 | pubmed:author | pubmed-author:RytzAndreasA | lld:pubmed |
| pubmed-article:12095422 | pubmed:author | pubmed-author:BergerAlvinA | lld:pubmed |
| pubmed-article:12095422 | pubmed:author | pubmed-author:MutchDavid... | lld:pubmed |
| pubmed-article:12095422 | pubmed:author | pubmed-author:RobertsMatthe... | lld:pubmed |
| pubmed-article:12095422 | pubmed:author | pubmed-author:MansourianRob... | lld:pubmed |
| pubmed-article:12095422 | pubmed:issnType | Electronic | lld:pubmed |
| pubmed-article:12095422 | pubmed:day | 21 | lld:pubmed |
| pubmed-article:12095422 | pubmed:volume | 3 | lld:pubmed |
| pubmed-article:12095422 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:12095422 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:12095422 | pubmed:pagination | 17 | lld:pubmed |
| pubmed-article:12095422 | pubmed:dateRevised | 2010-9-14 | lld:pubmed |
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| pubmed-article:12095422 | pubmed:year | 2002 | lld:pubmed |
| pubmed-article:12095422 | pubmed:articleTitle | The limit fold change model: a practical approach for selecting differentially expressed genes from microarray data. | lld:pubmed |
| pubmed-article:12095422 | pubmed:affiliation | Metabolic and Genomic Regulation, Nestlé Research Center, Vers-chez-les-Blanc, CH-1000 Lausanne 26, Switzerland. david.mutch@rdls.nestle.com | lld:pubmed |
| pubmed-article:12095422 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:12095422 | pubmed:publicationType | Validation Studies | lld:pubmed |
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