pubmed-article:19176548 | rdf:type | pubmed:Citation | lld:pubmed |
pubmed-article:19176548 | lifeskim:mentions | umls-concept:C0020792 | lld:lifeskim |
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pubmed-article:19176548 | lifeskim:mentions | umls-concept:C0040223 | lld:lifeskim |
pubmed-article:19176548 | lifeskim:mentions | umls-concept:C1511726 | lld:lifeskim |
pubmed-article:19176548 | lifeskim:mentions | umls-concept:C0552449 | lld:lifeskim |
pubmed-article:19176548 | lifeskim:mentions | umls-concept:C0443199 | lld:lifeskim |
pubmed-article:19176548 | lifeskim:mentions | umls-concept:C0205549 | lld:lifeskim |
pubmed-article:19176548 | pubmed:issue | 6 | lld:pubmed |
pubmed-article:19176548 | pubmed:dateCreated | 2009-3-13 | lld:pubmed |
pubmed-article:19176548 | pubmed:abstractText | In recent years, the biological literature has seen a significant increase of reported methods for identifying both structure and parameters of ordinary differential equations (ODEs) from time series data. A natural way to evaluate the performance of such methods is to try them on a sufficient number of realistic test cases. However, weak practices in specifying identification problems and lack of commonly accepted benchmark problems makes it difficult to evaluate and compare different methods. | lld:pubmed |
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pubmed-article:19176548 | pubmed:language | eng | lld:pubmed |
pubmed-article:19176548 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
pubmed-article:19176548 | pubmed:citationSubset | IM | lld:pubmed |
pubmed-article:19176548 | pubmed:status | MEDLINE | lld:pubmed |
pubmed-article:19176548 | pubmed:month | Mar | lld:pubmed |
pubmed-article:19176548 | pubmed:issn | 1367-4811 | lld:pubmed |
pubmed-article:19176548 | pubmed:author | pubmed-author:GennemarkPete... | lld:pubmed |
pubmed-article:19176548 | pubmed:author | pubmed-author:WedelinDagD | lld:pubmed |
pubmed-article:19176548 | pubmed:issnType | Electronic | lld:pubmed |
pubmed-article:19176548 | pubmed:day | 15 | lld:pubmed |
pubmed-article:19176548 | pubmed:volume | 25 | lld:pubmed |
pubmed-article:19176548 | pubmed:owner | NLM | lld:pubmed |
pubmed-article:19176548 | pubmed:authorsComplete | Y | lld:pubmed |
pubmed-article:19176548 | pubmed:pagination | 780-6 | lld:pubmed |
pubmed-article:19176548 | pubmed:dateRevised | 2009-11-18 | lld:pubmed |
pubmed-article:19176548 | pubmed:meshHeading | pubmed-meshheading:19176548... | lld:pubmed |
pubmed-article:19176548 | pubmed:meshHeading | pubmed-meshheading:19176548... | lld:pubmed |
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pubmed-article:19176548 | pubmed:meshHeading | pubmed-meshheading:19176548... | lld:pubmed |
pubmed-article:19176548 | pubmed:year | 2009 | lld:pubmed |
pubmed-article:19176548 | pubmed:articleTitle | Benchmarks for identification of ordinary differential equations from time series data. | lld:pubmed |
pubmed-article:19176548 | pubmed:affiliation | Department of Mathematical Sciences, University of Göteborg, SE-412 96 Göteborg, Sweden. peterg@chalmers.se | lld:pubmed |
pubmed-article:19176548 | pubmed:publicationType | Journal Article | lld:pubmed |
pubmed-article:19176548 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
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