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| pubmed-article:18173221 | pubmed:issue | 1 | lld:pubmed |
| pubmed-article:18173221 | pubmed:dateCreated | 2008-1-7 | lld:pubmed |
| pubmed-article:18173221 | pubmed:abstractText | Statistical tools enable unified analysis of data from multiple global proteomic experiments, producing unbiased estimates of normalization terms despite the missing data problem inherent in these studies. The modeling approach, implementation, and useful visualization tools are demonstrated via a case study of complex biological samples assessed using the iTRAQ relative labeling protocol. | lld:pubmed |
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| pubmed-article:18173221 | pubmed:language | eng | lld:pubmed |
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| pubmed-article:18173221 | pubmed:citationSubset | IM | lld:pubmed |
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| pubmed-article:18173221 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:18173221 | pubmed:month | Jan | lld:pubmed |
| pubmed-article:18173221 | pubmed:issn | 1535-3893 | lld:pubmed |
| pubmed-article:18173221 | pubmed:author | pubmed-author:ObergAnn LAL | lld:pubmed |
| pubmed-article:18173221 | pubmed:author | pubmed-author:MahoneyDougla... | lld:pubmed |
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| pubmed-article:18173221 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:18173221 | pubmed:volume | 7 | lld:pubmed |
| pubmed-article:18173221 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:18173221 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:18173221 | pubmed:pagination | 225-33 | lld:pubmed |
| pubmed-article:18173221 | pubmed:dateRevised | 2011-5-3 | lld:pubmed |
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| pubmed-article:18173221 | pubmed:year | 2008 | lld:pubmed |
| pubmed-article:18173221 | pubmed:articleTitle | Statistical analysis of relative labeled mass spectrometry data from complex samples using ANOVA. | lld:pubmed |
| pubmed-article:18173221 | pubmed:affiliation | Division of Biostatistics, Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, Minnesota 55905, USA. oberg.ann@mayo.edu | lld:pubmed |
| pubmed-article:18173221 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:18173221 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
| pubmed-article:18173221 | pubmed:publicationType | Research Support, N.I.H., Extramural | lld:pubmed |
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