| pubmed-article:11015595 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:11015595 | lifeskim:mentions | umls-concept:C1511726 | lld:lifeskim |
| pubmed-article:11015595 | lifeskim:mentions | umls-concept:C0017262 | lld:lifeskim |
| pubmed-article:11015595 | lifeskim:mentions | umls-concept:C0162586 | lld:lifeskim |
| pubmed-article:11015595 | lifeskim:mentions | umls-concept:C0449445 | lld:lifeskim |
| pubmed-article:11015595 | pubmed:issue | 1 | lld:pubmed |
| pubmed-article:11015595 | pubmed:dateCreated | 2000-10-13 | lld:pubmed |
| pubmed-article:11015595 | pubmed:abstractText | We have developed a novel algorithm for analyzing gene expression data. This algorithm uses fuzzy logic to transform expression values into qualitative descriptors that can be evaluated by using a set of heuristic rules. In our tests we designed a model to find triplets of activators, repressors, and targets in a yeast gene expression data set. For the conditions tested, the predictions made by the algorithm agree well with experimental data in the literature. The algorithm can also assist in determining the function of uncharacterized proteins and is able to detect a substantially larger number of transcription factors than could be found at random. This technology extends current techniques such as clustering in that it allows the user to generate a connected network of genes using only expression data. | lld:pubmed |
| pubmed-article:11015595 | pubmed:language | eng | lld:pubmed |
| pubmed-article:11015595 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:11015595 | pubmed:citationSubset | IM | lld:pubmed |
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| pubmed-article:11015595 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:11015595 | pubmed:month | Jun | lld:pubmed |
| pubmed-article:11015595 | pubmed:issn | 1531-2267 | lld:pubmed |
| pubmed-article:11015595 | pubmed:author | pubmed-author:WangYY | lld:pubmed |
| pubmed-article:11015595 | pubmed:author | pubmed-author:WoolfP JPJ | lld:pubmed |
| pubmed-article:11015595 | pubmed:issnType | Electronic | lld:pubmed |
| pubmed-article:11015595 | pubmed:day | 29 | lld:pubmed |
| pubmed-article:11015595 | pubmed:volume | 3 | lld:pubmed |
| pubmed-article:11015595 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:11015595 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:11015595 | pubmed:pagination | 9-15 | lld:pubmed |
| pubmed-article:11015595 | pubmed:dateRevised | 2006-11-15 | lld:pubmed |
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| pubmed-article:11015595 | pubmed:year | 2000 | lld:pubmed |
| pubmed-article:11015595 | pubmed:articleTitle | A fuzzy logic approach to analyzing gene expression data. | lld:pubmed |
| pubmed-article:11015595 | pubmed:affiliation | Bioinformatics, Department of Molecular Biology, Parke-Davis Pharmaceutical Research, Warner-Lanbert, Ann Arbor 48105, USA. | lld:pubmed |
| pubmed-article:11015595 | pubmed:publicationType | Journal Article | lld:pubmed |
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