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

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

6 Dyfyniadau(SciVal)

Crynodeb

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.
Iaith wreiddiolSaesneg
TeitlSecond International Symposium, CompLife 2006, Cambridge, UK, September 27-29, 2006. Proceedings
CyhoeddwrSpringer Nature
Tudalennau226-235
Nifer y tudalennau10
ISBN (Electronig)978-3-540-45768-8
ISBN (Argraffiad)978-3-540-45767-1
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 2006
DigwyddiadInternational Symposium, CompLife - Cambridge, Teyrnas Unedig Prydain Fawr a Gogledd Iwerddon
Hyd: 27 Medi 200629 Medi 2006

Cynhadledd

CynhadleddInternational Symposium, CompLife
Gwlad/TiriogaethTeyrnas Unedig Prydain Fawr a Gogledd Iwerddon
DinasCambridge
Cyfnod27 Medi 200629 Medi 2006

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