On the interpretation of high throughput MS based metabolomics fingerprints with Random Forest

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dc.contributor.author Enot, David P.
dc.contributor.author Beckmann, Manfred
dc.contributor.author Draper, John
dc.date.accessioned 2009-08-12T08:19:14Z
dc.date.available 2009-08-12T08:19:14Z
dc.date.issued 2006
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 Berlin , pp. 226-235 , International Symposium, CompLife , Cambridge , United Kingdom , 27-29 September . , 10.1007/11875741_22 en
dc.identifier.citation conference en
dc.identifier.isbn 978-3-540-45767-1
dc.identifier.isbn 978-3-540-45768-8
dc.identifier.other PURE: 114179
dc.identifier.other dspace: 2160/2855
dc.identifier.uri http://hdl.handle.net/2160/2855
dc.identifier.uri http://www.springerlink.com/content/646m13v201657752/ en
dc.description Enot, D.P., Beckmann, M., Draper, J. (2006). On the interpretation of high throughput MS based metabolomics fingerprints with Random Forest. Proceedings, 2nd International Symposium on Computational Life Science - CompLife 06, 4216, 226-235 en
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
dc.format.extent 10 en
dc.language.iso eng
dc.publisher Springer Berlin
dc.relation.ispartof Second International Symposium, CompLife 2006, Cambridge, UK, September 27-29, 2006. Proceedings en
dc.title On the interpretation of high throughput MS based metabolomics fingerprints with Random Forest en
dc.type Text en
dc.type.publicationtype Book chapter en
dc.identifier.doi http://dx.doi.org/10.1007/11875741_22
dc.contributor.institution Institute of Biological, Environmental and Rural Sciences en


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