| pubmed-article:18718939 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:18718939 | lifeskim:mentions | umls-concept:C0026339 | lld:lifeskim |
| pubmed-article:18718939 | lifeskim:mentions | umls-concept:C0026336 | lld:lifeskim |
| pubmed-article:18718939 | lifeskim:mentions | umls-concept:C0017337 | lld:lifeskim |
| pubmed-article:18718939 | lifeskim:mentions | umls-concept:C1280500 | lld:lifeskim |
| pubmed-article:18718939 | lifeskim:mentions | umls-concept:C0183185 | lld:lifeskim |
| pubmed-article:18718939 | pubmed:issue | 21 | lld:pubmed |
| pubmed-article:18718939 | pubmed:dateCreated | 2008-10-21 | lld:pubmed |
| pubmed-article:18718939 | pubmed:abstractText | Nested effects models (NEMs) are a class of probabilistic models introduced to analyze the effects of gene perturbation screens visible in high-dimensional phenotypes like microarrays or cell morphology. NEMs reverse engineer upstream/downstream relations of cellular signaling cascades. NEMs take as input a set of candidate pathway genes and phenotypic profiles of perturbing these genes. NEMs return a pathway structure explaining the observed perturbation effects. Here, we describe the package nem, an open-source software to efficiently infer NEMs from data. Our software implements several search algorithms for model fitting and is applicable to a wide range of different data types and representations. The methods we present summarize the current state-of-the-art in NEMs. AVAILABILITY: Our software is written in the R language and freely avail-able via the Bioconductor project at http://www.bioconductor.org. | lld:pubmed |
| pubmed-article:18718939 | pubmed:grant | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:18718939 | pubmed:grant | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:18718939 | pubmed:commentsCorrections | http://linkedlifedata.com/r... | lld:pubmed |
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| pubmed-article:18718939 | pubmed:commentsCorrections | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:18718939 | pubmed:commentsCorrections | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:18718939 | pubmed:commentsCorrections | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:18718939 | pubmed:commentsCorrections | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:18718939 | pubmed:commentsCorrections | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:18718939 | pubmed:language | eng | lld:pubmed |
| pubmed-article:18718939 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:18718939 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:18718939 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:18718939 | pubmed:month | Nov | lld:pubmed |
| pubmed-article:18718939 | pubmed:issn | 1367-4811 | lld:pubmed |
| pubmed-article:18718939 | pubmed:author | pubmed-author:BeissbarthTim... | lld:pubmed |
| pubmed-article:18718939 | pubmed:author | pubmed-author:SpangRainerR | lld:pubmed |
| pubmed-article:18718939 | pubmed:author | pubmed-author:FröhlichHolge... | lld:pubmed |
| pubmed-article:18718939 | pubmed:author | pubmed-author:KostkaDennisD | lld:pubmed |
| pubmed-article:18718939 | pubmed:author | pubmed-author:MarkowetzFF | lld:pubmed |
| pubmed-article:18718939 | pubmed:author | pubmed-author:TreschAchimA | lld:pubmed |
| pubmed-article:18718939 | pubmed:author | pubmed-author:JacobJubyJ | lld:pubmed |
| pubmed-article:18718939 | pubmed:issnType | Electronic | lld:pubmed |
| pubmed-article:18718939 | pubmed:day | 1 | lld:pubmed |
| pubmed-article:18718939 | pubmed:volume | 24 | lld:pubmed |
| pubmed-article:18718939 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:18718939 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:18718939 | pubmed:pagination | 2549-50 | lld:pubmed |
| pubmed-article:18718939 | pubmed:dateRevised | 2010-9-21 | lld:pubmed |
| pubmed-article:18718939 | pubmed:meshHeading | pubmed-meshheading:18718939... | lld:pubmed |
| pubmed-article:18718939 | pubmed:meshHeading | pubmed-meshheading:18718939... | lld:pubmed |
| pubmed-article:18718939 | pubmed:meshHeading | pubmed-meshheading:18718939... | lld:pubmed |
| pubmed-article:18718939 | pubmed:meshHeading | pubmed-meshheading:18718939... | lld:pubmed |
| pubmed-article:18718939 | pubmed:meshHeading | pubmed-meshheading:18718939... | lld:pubmed |
| pubmed-article:18718939 | pubmed:meshHeading | pubmed-meshheading:18718939... | lld:pubmed |
| pubmed-article:18718939 | pubmed:meshHeading | pubmed-meshheading:18718939... | lld:pubmed |
| pubmed-article:18718939 | pubmed:year | 2008 | lld:pubmed |
| pubmed-article:18718939 | pubmed:articleTitle | Analyzing gene perturbation screens with nested effects models in R and bioconductor. | lld:pubmed |
| pubmed-article:18718939 | pubmed:affiliation | German Cancer Research Center, INF 580, 69120 Heidelberg, Germany. | lld:pubmed |
| pubmed-article:18718939 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:18718939 | pubmed:publicationType | Research Support, U.S. Gov't, Non-P.H.S. | lld:pubmed |
| pubmed-article:18718939 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
| pubmed-article:18718939 | pubmed:publicationType | Research Support, N.I.H., Extramural | lld:pubmed |
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