| pubmed-article:18837969 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:18837969 | lifeskim:mentions | umls-concept:C0008139 | lld:lifeskim |
| pubmed-article:18837969 | lifeskim:mentions | umls-concept:C0040223 | lld:lifeskim |
| pubmed-article:18837969 | lifeskim:mentions | umls-concept:C0037585 | lld:lifeskim |
| pubmed-article:18837969 | lifeskim:mentions | umls-concept:C0681814 | lld:lifeskim |
| pubmed-article:18837969 | lifeskim:mentions | umls-concept:C1709016 | lld:lifeskim |
| pubmed-article:18837969 | lifeskim:mentions | umls-concept:C0205549 | lld:lifeskim |
| pubmed-article:18837969 | pubmed:dateCreated | 2008-11-5 | lld:pubmed |
| pubmed-article:18837969 | pubmed:abstractText | Gene expression levels in a given cell can be influenced by different factors, namely pharmacological or medical treatments. The response to a given stimulus is usually different for different genes and may depend on time. One of the goals of modern molecular biology is the high-throughput identification of genes associated with a particular treatment or a biological process of interest. From methodological and computational point of view, analyzing high-dimensional time course microarray data requires very specific set of tools which are usually not included in standard software packages. Recently, the authors of this paper developed a fully Bayesian approach which allows one to identify differentially expressed genes in a 'one-sample' time-course microarray experiment, to rank them and to estimate their expression profiles. The method is based on explicit expressions for calculations and, hence, very computationally efficient. | lld:pubmed |
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| pubmed-article:18837969 | pubmed:language | eng | lld:pubmed |
| pubmed-article:18837969 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:18837969 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:18837969 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:18837969 | pubmed:issn | 1471-2105 | lld:pubmed |
| pubmed-article:18837969 | pubmed:author | pubmed-author:CutilloLuisaL | lld:pubmed |
| pubmed-article:18837969 | pubmed:author | pubmed-author:AngeliniClaud... | lld:pubmed |
| pubmed-article:18837969 | pubmed:author | pubmed-author:De... | lld:pubmed |
| pubmed-article:18837969 | pubmed:author | pubmed-author:MutarelliMarg... | lld:pubmed |
| pubmed-article:18837969 | pubmed:author | pubmed-author:PenskyMariann... | lld:pubmed |
| pubmed-article:18837969 | pubmed:issnType | Electronic | lld:pubmed |
| pubmed-article:18837969 | pubmed:volume | 9 | lld:pubmed |
| pubmed-article:18837969 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:18837969 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:18837969 | pubmed:pagination | 415 | lld:pubmed |
| pubmed-article:18837969 | pubmed:dateRevised | 2009-11-18 | lld:pubmed |
| pubmed-article:18837969 | pubmed:meshHeading | pubmed-meshheading:18837969... | lld:pubmed |
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| pubmed-article:18837969 | pubmed:meshHeading | pubmed-meshheading:18837969... | lld:pubmed |
| pubmed-article:18837969 | pubmed:year | 2008 | lld:pubmed |
| pubmed-article:18837969 | pubmed:articleTitle | BATS: a Bayesian user-friendly software for analyzing time series microarray experiments. | lld:pubmed |
| pubmed-article:18837969 | pubmed:affiliation | Istituto per le Applicazioni del Calcolo, Mauro Picone, CNR-Napoli, Italy. c.angelini@iac.cnr.it | lld:pubmed |
| pubmed-article:18837969 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:18837969 | pubmed:publicationType | Research Support, U.S. Gov't, Non-P.H.S. | lld:pubmed |
| pubmed-article:18837969 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
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