Source:http://linkedlifedata.com/resource/pubmed/id/21771315
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:dateCreated |
2011-8-1
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| pubmed:abstractText |
Network inference from high-throughput data has become an important means of current analysis of biological systems. For instance, in cancer research, the functional relationships of cancer related proteins, summarised into signalling networks are of central interest for the identification of pathways that influence tumour development. Cancer cell lines can be used as model systems to study the cellular response to drug treatments in a time-resolved way. Based on these kind of data, modelling approaches for the signalling relationships are needed, that allow to generate hypotheses on potential interference points in the networks.
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| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:citationSubset |
IM
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| pubmed:status |
MEDLINE
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| pubmed:issn |
1471-2105
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| pubmed:author | |
| pubmed:issnType |
Electronic
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| pubmed:volume |
12
|
| pubmed:owner |
NLM
|
| pubmed:authorsComplete |
Y
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| pubmed:pagination |
291
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| pubmed:meshHeading |
pubmed-meshheading:21771315-Algorithms,
pubmed-meshheading:21771315-Breast Neoplasms,
pubmed-meshheading:21771315-Cell Line, Tumor,
pubmed-meshheading:21771315-Humans,
pubmed-meshheading:21771315-Longitudinal Studies,
pubmed-meshheading:21771315-Markov Chains,
pubmed-meshheading:21771315-Models, Biological,
pubmed-meshheading:21771315-Monte Carlo Method,
pubmed-meshheading:21771315-Signal Transduction,
pubmed-meshheading:21771315-Software
|
| pubmed:year |
2011
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| pubmed:articleTitle |
Inferring signalling networks from longitudinal data using sampling based approaches in the R-package 'ddepn'.
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| pubmed:affiliation |
German Cancer Research Center (DKFZ), Division of Molecular Genome Analysis, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany. c.bender@dkfz-heidelberg.de
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| pubmed:publicationType |
Journal Article,
Research Support, Non-U.S. Gov't
|