Predicting retention time in hydrophilic interaction liquid chromatography mass spectrometry and its use for peak annotation in metabolomics-Reference-Cited by-同舟云学术

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Predicting retention time in hydrophilic interaction liquid chromatography mass spectrometry and its use for peak annotation in metabolomics

Published:2014-09-07 Issue:3 Volume:11 Page:696-706
ISSN:1573-3882
Container-title:Metabolomics
language:en
Short-container-title:Metabolomics

Author:

Cao Mingshu,Fraser Karl,Huege Jan,Featonby Tom,Rasmussen Susanne,Jones Chris

Publisher

Springer Science and Business Media LLC

Subject

Clinical Biochemistry,Biochemistry,Endocrinology, Diabetes and Metabolism

Link

http://link.springer.com/content/pdf/10.1007/s11306-014-0727-x.pdf

Reference44 articles.

1. Boswell, P. G., Schellenberg, J. R., Carr, P. W., Cohen, J. D., & Hegeman, A. D. (2011). A study on retention “projection” as a supplementary means for compound identification by liquid chromatography–mass spectrometry capable of predicting retention with different gradients, flow rates, and instruments. Journal of Chromatography A, 1218(38), 6732–6741. doi: 10.1016/j.chroma.2011.07.105 .

2. Breiman, L. (2001a). Random Forests. Machine Learning, 45(1), 5–32. doi: 10.1023/a:1010933404324 .

3. Breiman, L. (2001b). Statistical modeling: the two cultures. Statistical Science, 16(3), 199–231.

4. Cao, M., Fraser, K., & Rasmussen, S. (2013). Computational analyses of spectral trees from electrospray multi-stage mass spectrometry to aid metabolite identification. Metabolites, 3(4), 1036–1050.

5. Cheng, T., Zhao, Y., Li, X., Lin, F., Xu, Y., Zhang, X., et al. (2007). Computation of octanol–water partition coefficients by guiding an additive model with knowledge. Journal of Chemical Information and Modeling, 47(6), 2140–2148. doi: 10.1021/ci700257y .

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