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dc.contributor.author David P. en_US
dc.contributor.author Manfred en_US
dc.contributor.author John en_US
dc.date.accessioned 2009-08-12T08:19:14Z
dc.date.available 2009-08-12T08:19:14Z
dc.date.issued 2006 en_US
dc.identifier http://dx.doi.org/10.1007/11875741_22 en_US
dc.identifier 978-3-540-45767-1 en_US
dc.identifier 978-3-540-45768-8 en_US
dc.identifier.citation Enot , D P , Beckmann , M & Draper , J 2006 , ' On the interpretation of high throughput MS based metabolomics fingerprints with Random Forest ' . in Second International Symposium, CompLife 2006, Cambridge, UK, September 27-29, 2006. Proceedings . Springer Science+Business Media , pp. 226-235 , International Symposium, CompLife , Cambridge , United Kingdom , 27-29 September . , 10.1007/11875741_22 en_US
dc.identifier.citation conference en_US
dc.identifier.other PURE: 114179 en_US
dc.identifier.other dspace: 2160/2855 en_US
dc.identifier.uri http://hdl.handle.net/2160/2855
dc.description.abstract We discuss application of a machine learning method, Random Forest (RF), for the extraction of relevant biological knowledge from metabolomics fingerprinting experiments. The importance of RF margins and variable significance as well as prediction accuracy is discussed to provide insight into model generalisability and explanatory power. A method is described for detection of relevant features while conserving the redundant structure of the fingerprint data. The methodology is illustrated using two datasets from electrospray ionisation mass spectrometry from 27 Arabidopsis genotypes and a set of transgenic potato lines. en_US
dc.format.extent 10 en_US
dc.publisher Springer Science+Business Media en_US
dc.relation.ispartof Second International Symposium, CompLife 2006, Cambridge, UK, September 27-29, 2006. Proceedings en_US
dc.title On the interpretation of high throughput MS based metabolomics fingerprints with Random Forest en_US
dc.contributor.pbl Institute of Biological, Environmental and Rural Sciences en_US


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