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

David P. Enot; Manfred Beckmann; John Draper

doi:10.1007/11875741_22

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

David P. Enot
, Manfred Beckmann
, John Draper

Athrofa'r Gwyddorau Biolegol, Amgylcheddol a Gwledig

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Trafodion Cynhadledd (ISBN)

6 Dyfyniadau (Scopus)

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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 wreiddiol	Saesneg
Teitl	Second International Symposium, CompLife 2006, Cambridge, UK, September 27-29, 2006. Proceedings
Cyhoeddwr	Springer Nature
Tudalennau	226-235
Nifer y tudalennau	10
ISBN (Electronig)	978-3-540-45768-8
ISBN (Argraffiad)	978-3-540-45767-1
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1007/11875741_22
Statws	Cyhoeddwyd - 2006
Digwyddiad	International Symposium, CompLife - Cambridge, Teyrnas Unedig Prydain Fawr a Gogledd Iwerddon Hyd: 27 Medi 2006 → 29 Medi 2006

Cynhadledd

Cynhadledd	International Symposium, CompLife
Gwlad/Tiriogaeth	Teyrnas Unedig Prydain Fawr a Gogledd Iwerddon
Dinas	Cambridge
Cyfnod	27 Medi 2006 → 29 Medi 2006

Mynediad at Ddogfen

10.1007/11875741_22

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@inproceedings{9596308824b44c0293d6ad93cd82a466,

title = "On the interpretation of high throughput MS based metabolomics fingerprints with Random Forest",

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.",

author = "Enot, \{David P.\} and Manfred Beckmann and John Draper",

note = "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; International Symposium, CompLife ; Conference date: 27-09-2006 Through 29-09-2006",

year = "2006",

doi = "10.1007/11875741\_22",

language = "English",

isbn = "978-3-540-45767-1",

pages = "226--235",

booktitle = "Second International Symposium, CompLife 2006, Cambridge, UK, September 27-29, 2006. Proceedings",

publisher = "Springer Nature",

address = "Switzerland",

}

Enot, DP, Beckmann, M & Draper, J 2006, On the interpretation of high throughput MS based metabolomics fingerprints with Random Forest. yn Second International Symposium, CompLife 2006, Cambridge, UK, September 27-29, 2006. Proceedings. Springer Nature, tt. 226-235, International Symposium, CompLife, Cambridge, Teyrnas Unedig Prydain Fawr a Gogledd Iwerddon, 27 Medi 2006. https://doi.org/10.1007/11875741_22

On the interpretation of high throughput MS based metabolomics fingerprints with Random Forest. / Enot, David P.; Beckmann, Manfred; Draper, John.
Second International Symposium, CompLife 2006, Cambridge, UK, September 27-29, 2006. Proceedings. Springer Nature, 2006. t. 226-235.

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Trafodion Cynhadledd (ISBN)

TY - GEN

T1 - On the interpretation of high throughput MS based metabolomics fingerprints with Random Forest

AU - Enot, David P.

AU - Beckmann, Manfred

AU - Draper, John

N1 - 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

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

U2 - 10.1007/11875741_22

DO - 10.1007/11875741_22

M3 - Conference Proceeding (ISBN)

SN - 978-3-540-45767-1

SP - 226

EP - 235

BT - Second International Symposium, CompLife 2006, Cambridge, UK, September 27-29, 2006. Proceedings

PB - Springer Nature

T2 - International Symposium, CompLife

Y2 - 27 September 2006 through 29 September 2006

ER -

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

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