| pubmed-article:10902190 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:10902190 | lifeskim:mentions | umls-concept:C1882071 | lld:lifeskim |
| pubmed-article:10902190 | lifeskim:mentions | umls-concept:C0242485 | lld:lifeskim |
| pubmed-article:10902190 | lifeskim:mentions | umls-concept:C0376522 | lld:lifeskim |
| pubmed-article:10902190 | lifeskim:mentions | umls-concept:C0009085 | lld:lifeskim |
| pubmed-article:10902190 | lifeskim:mentions | umls-concept:C1533716 | lld:lifeskim |
| pubmed-article:10902190 | lifeskim:mentions | umls-concept:C1709100 | lld:lifeskim |
| pubmed-article:10902190 | lifeskim:mentions | umls-concept:C2347946 | lld:lifeskim |
| pubmed-article:10902190 | lifeskim:mentions | umls-concept:C0872316 | lld:lifeskim |
| pubmed-article:10902190 | pubmed:dateCreated | 2000-8-29 | lld:pubmed |
| pubmed-article:10902190 | pubmed:abstractText | Increasing numbers of methodologies are available to find functional genomic clusters in RNA expression data. We describe a technique that computes comprehensive pair-wise mutual information for all genes in such a data set. An association with a high mutual information means that one gene is non-randomly associated with another; we hypothesize this means the two are related biologically. By picking a threshold mutual information and using only associations at or above the threshold, we show how this technique was used on a public data set of 79 RNA expression measurements of 2,467 genes to construct 22 clusters, or Relevance Networks. The biological significance of each Relevance Network is explained. | lld:pubmed |
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| pubmed-article:10902190 | pubmed:grant | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:10902190 | pubmed:language | eng | lld:pubmed |
| pubmed-article:10902190 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:10902190 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:10902190 | pubmed:chemical | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:10902190 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:10902190 | pubmed:issn | 1793-5091 | lld:pubmed |
| pubmed-article:10902190 | pubmed:author | pubmed-author:KohaneI SIS | lld:pubmed |
| pubmed-article:10902190 | pubmed:author | pubmed-author:ButteA JAJ | lld:pubmed |
| pubmed-article:10902190 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:10902190 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:10902190 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:10902190 | pubmed:pagination | 418-29 | lld:pubmed |
| pubmed-article:10902190 | pubmed:dateRevised | 2007-11-14 | lld:pubmed |
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| pubmed-article:10902190 | pubmed:meshHeading | pubmed-meshheading:10902190... | lld:pubmed |
| pubmed-article:10902190 | pubmed:year | 2000 | lld:pubmed |
| pubmed-article:10902190 | pubmed:articleTitle | Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. | lld:pubmed |
| pubmed-article:10902190 | pubmed:affiliation | Children's Hospital Informatics Program, Boston, MA 02115, USA. | lld:pubmed |
| pubmed-article:10902190 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:10902190 | pubmed:publicationType | Research Support, U.S. Gov't, P.H.S. | lld:pubmed |
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