Detecting a difference – assessing generalisability when modelling metabolome fingerprint data in longer term studies of genetically modified plants-Reference-Cited by-同舟云学术

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Detecting a difference – assessing generalisability when modelling metabolome fingerprint data in longer term studies of genetically modified plants

Published:2007-06-19 Issue:3 Volume:3 Page:335-347
ISSN:1573-3882
Container-title:Metabolomics
language:en
Short-container-title:Metabolomics

Author:

Enot David P.,Beckmann Manfred,Draper John

Publisher

Springer Science and Business Media LLC

Subject

Clinical Biochemistry,Biochemistry,Endocrinology, Diabetes and Metabolism

Link

http://link.springer.com/content/pdf/10.1007/s11306-007-0064-4.pdf

Reference54 articles.

1. Allen J., Davey H.M., Broadhurst D., Heald J.K., Rowland J.J., Oliver S.G., Kell D.B. (2003) High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. Nat. Biotechnol. 21:692–696

2. Bino R.J., Hall R.D., Fiehn O., Kopka J., Saito K., Draper J., Nikolau B.J., Mendes P., Roessner-Tunali U., Beale M.H., Trethewey R.N., Lange B.M., Wurtele E.S., Sumner L.W. (2004) Potential of metabolomics as a functional genomics tool. Trends Plant Sci. 9:418–425

3. Breiman L. (2001) Random forests. Machine Learning 45:5–32

4. Breiman L. (2001) Statistical modeling: the two cultures. Statis. Sci. 16:199–215

5. Breiman, L. (2003). Two-eyed algorithms and problems. LECTURE NOTES IN COMPUTER SCIENCE, 9–9

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