- About this Journal ·
- Abstracting and Indexing ·
- Advance Access ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Evidence-Based Complementary and Alternative Medicine
Volume 2013 (2013), Article ID 298183, 11 pages
Random Forest in Clinical Metabolomics for Phenotypic Discrimination and Biomarker Selection
Center for Translational Medicine and Shanghai Key Laboratory of Diabetes Mellitus, Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200233, China
Received 10 November 2012; Accepted 11 December 2012
Academic Editor: Wei Jia
Copyright © 2013 Tianlu Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- O. Fiehn, “Metabolomics—the link between genotypes and phenotypes,” Plant Molecular Biology, vol. 48, no. 1-2, pp. 155–171, 2002.
- J. K. Nicholson, J. Connelly, J. C. Lindon, and E. Holmes, “Metabonomics: a platform for studying drug toxicity and gene function,” Nature Reviews Drug Discovery, vol. 1, no. 2, pp. 153–161, 2002.
- J. K. Nicholson, J. C. Lindon, and E. Holmes, “‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data,” Xenobiotica, vol. 29, no. 11, pp. 1181–1189, 1999.
- J. Schnabel, “Targeting tumour metabolism,” Nature Reviews Drug Discovery, vol. 9, pp. 503–504, 2010.
- T. L. Chen, G. X. Xie, X. Y. Wang, et al., “Serum and urinemetabolite profiling reveals potential biomarkers of human hepatocellular carcinoma,” Molecular & Cellular Proteomics, vol. 10, pp. 1–13, 2011.
- G. X. Xie, X. J. Zheng, X. Qi, et al., “Metabonomic evaluation of melamine-induced acute renal toxicity in rats,” Journal of Proteome Research, vol. 9, no. 1, pp. 125–133, 2010.
- J. Yang, T. Chen, L. Sun, et al., “Potential metabolite markers of schizophrenia,” Molecular Psychiatry, vol. 18, no. 1, pp. 67–78, 2013.
- Y. Q. Bao, T. Zhao, X. Y. Wang, et al., “Metabonomic variations in the drug-treated type 2 diabetes mellitus patients and healthy volunteers,” Journal of Proteome Research, vol. 8, no. 4, pp. 1623–1630, 2009.
- X. Wang, J. Lin, T. Chen, M. Zhou, M. Su, and W. Jia, “Metabolic profiling reveals the protective effect of diammonium glycyrrhizinate on acute hepatic injury induced by carbon tetrachloride,” Metabolomics, vol. 7, no. 2, pp. 226–236, 2010.
- J. Trygg, E. Holmes, and T. Lundstedt, “Chemometrics in metabonomics,” Journal of Proteome Research, vol. 6, no. 2, pp. 469–479, 2007.
- H. W. Cho, S. B. Kim, M. K. Jeong, et al., “Discovery of metabolite features for the modelling and analysis of high-resolution NMR spectra,” International Journal of Data Mining and Bioinformatics, vol. 2, no. 2, pp. 176–192, 2008.
- Z. Cai, J. Zhao, X. Wang, et al., “A combined proteomics and metabolomics profiling of gastric cardia cancer reveals characteristic dysregulations in glucose metabolism,” Molecular & Cellular Proteomics, vol. 9, pp. 2617–2628, 2010.
- X. Li, S. Yang, Y. Qiu, et al., “Urinary metabolomics as a potentially novel diagnostic and stratification tool for knee osteoarthritis,” Metabolomics, vol. 6, no. 1, pp. 109–118, 2010.
- J. Wei, G. X. Xie, Z. T. Zhou, et al., “Salivary metabolite signatures of oral cancer and leukoplakia,” International Journal of Cancer, vol. 129, no. 9, pp. 2207–2217, 2011.
- D. Amaratunga, J. Cabrera, and Y. S. Lee, “Enriched random forests,” Bioinformatics, vol. 24, no. 18, pp. 2010–2014, 2008.
- A. Statnikov, L. Wang, and C. F. Aliferis, “A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification,” BMC Bioinformatics, vol. 9, pp. 319–328, 2008.
- X. Y. Wu, Z. Y. Wu, and K. Li, “Identification of differential gene expression for microarray data using recursive random forest,” Chinese Medical Journal, vol. 121, no. 24, pp. 2492–2496, 2008.
- A. Acharjeea, B. Kloosterman, R. C. H. D. Vos, et al., “Data integration and network reconstruction with ~omics data using Random Forest regression in potato,” Analytica Chimica Acta, vol. 705, no. 1-2, pp. 56–63, 2011.
- R. Jiang, W. W. Tang, X. B. Wu, and W. H. Fu, “A random forest approach to the detection of epistatic interactions in case-control studies,” BMC Bioinformatics, vol. 10, supplement 1, pp. 65–76, 2009.
- A. Jemal, R. Siegel, J. Xu, and E. Ward, “Cancer statistics, 2010,” CA Cancer Journal for Clinicians, vol. 60, no. 5, pp. 277–300, 2010.
- Y. Qiu, M. Su, Y. Liu, et al., “Application of ethyl chloroformate derivatization for gas chromatography-mass spectrometry based metabonomic profiling,” Analytica Chimica Acta, vol. 583, no. 2, pp. 277–283, 2007.
- L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
- A. P. Bradley, “The use of the area under the ROC curve in the evaluation of machine learning algorithms,” Pattern Recognition, vol. 30, no. 7, pp. 1145–1159, 1997.
- K. Duan, S. S. Keerthi, and A. N. Poo, “Evaluation of simple performance measures for tuning SVM hyperparameters,” Neurocomputing, vol. 51, pp. 41–59, 2003.
- I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene selection for cancer classification using support vector machines,” Machine Learning, vol. 46, no. 1–3, pp. 389–422, 2002.
- Y. P. Qiu, G. X. Cai, M. M. Su, et al., “Serum metabolite profiling of human colorectal cancer using GC−TOFMS and UPLC−QTOFMS,” Journal of Proteome Research, vol. 8, no. 10, pp. 4844–4850, 2009.