| pubmed-article:14718068 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:14718068 | lifeskim:mentions | umls-concept:C0087111 | lld:lifeskim |
| pubmed-article:14718068 | lifeskim:mentions | umls-concept:C0332285 | lld:lifeskim |
| pubmed-article:14718068 | lifeskim:mentions | umls-concept:C1511726 | lld:lifeskim |
| pubmed-article:14718068 | lifeskim:mentions | umls-concept:C0185125 | lld:lifeskim |
| pubmed-article:14718068 | lifeskim:mentions | umls-concept:C0887808 | lld:lifeskim |
| pubmed-article:14718068 | lifeskim:mentions | umls-concept:C1708729 | lld:lifeskim |
| pubmed-article:14718068 | pubmed:dateCreated | 2004-5-11 | lld:pubmed |
| pubmed-article:14718068 | pubmed:abstractText | To cancel experimental variations, microarray data must be normalized prior to analysis. Where an appropriate model for statistical data distribution is available, a parametric method can normalize a group of data sets that have common distributions. Although such models have been proposed for microarray data, they have not always fit the distribution of real data and thus have been inappropriate for normalization. Consequently, microarray data in most cases have been normalized with non-parametric methods that adjust data in a pair-wise manner. However, data analysis and the integration of resultant knowledge among experiments have been difficult, since such normalization concepts lack a universal standard. | lld:pubmed |
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| pubmed-article:14718068 | pubmed:language | eng | lld:pubmed |
| pubmed-article:14718068 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:14718068 | pubmed:citationSubset | IM | lld:pubmed |
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| pubmed-article:14718068 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:14718068 | pubmed:month | Jan | lld:pubmed |
| pubmed-article:14718068 | pubmed:issn | 1471-2105 | lld:pubmed |
| pubmed-article:14718068 | pubmed:author | pubmed-author:KonishiTomoka... | lld:pubmed |
| pubmed-article:14718068 | pubmed:issnType | Electronic | lld:pubmed |
| pubmed-article:14718068 | pubmed:day | 13 | lld:pubmed |
| pubmed-article:14718068 | pubmed:volume | 5 | lld:pubmed |
| pubmed-article:14718068 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:14718068 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:14718068 | pubmed:pagination | 5 | lld:pubmed |
| pubmed-article:14718068 | pubmed:dateRevised | 2009-11-18 | lld:pubmed |
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| pubmed-article:14718068 | pubmed:year | 2004 | lld:pubmed |
| pubmed-article:14718068 | pubmed:articleTitle | Three-parameter lognormal distribution ubiquitously found in cDNA microarray data and its application to parametric data treatment. | lld:pubmed |
| pubmed-article:14718068 | pubmed:affiliation | Faculty of Bioresource Sciences, Akita Prefectural University, Akita 010-0195, Japan. konishi@akita-pu.ac.jp | lld:pubmed |
| pubmed-article:14718068 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:14718068 | pubmed:publicationType | Comparative Study | lld:pubmed |
| pubmed-article:14718068 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
| pubmed-article:14718068 | pubmed:publicationType | Evaluation Studies | lld:pubmed |
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