Weighted variable kernel support vector machine classifier for metabolomics data analysis

[1]  John C. Lindon,et al.  NMR Spectroscopy of Biofluids , 1999 .

[2]  Dong-Sheng Cao,et al.  A new strategy of exploring metabolomics data using Monte Carlo tree. , 2011, The Analyst.

[3]  Qing-Song Xu,et al.  Support vector machines and its applications in chemistry , 2009 .

[4]  Xin Chen,et al.  Effect of Molecular Descriptor Feature Selection in Support Vector Machine Classification of Pharmacokinetic and Toxicological Properties of Chemical Agents , 2004, J. Chem. Inf. Model..

[5]  Kishore K. Pasikanti,et al.  Gas chromatography/mass spectrometry in metabolic profiling of biological fluids. , 2008, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

[6]  O. Fiehn,et al.  Mass spectrometry-based metabolic profiling reveals different metabolite patterns in invasive ovarian carcinomas and ovarian borderline tumors. , 2006, Cancer research.

[7]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[8]  S. Wold,et al.  Principal component analysis of multivariate images , 1989 .

[9]  Hongdong Li,et al.  Identification of free fatty acids profiling of type 2 diabetes mellitus and exploring possible biomarkers by GC–MS coupled with chemometrics , 2010, Metabolomics.

[10]  Menghui H. Zhang,et al.  Application of boosting to classification problems in chemometrics , 2005 .

[11]  Roman M. Balabin,et al.  Biodiesel classification by base stock type (vegetable oil) using near infrared spectroscopy data. , 2011, Analytica chimica acta.

[12]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[13]  Yizeng Liang,et al.  A novel kernel Fisher discriminant analysis: constructing informative kernel by decision tree ensemble for metabolomics data analysis. , 2011, Analytica chimica acta.

[14]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[15]  Roman M. Balabin,et al.  Support vector machine regression (LS-SVM)--an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data? , 2011, Physical chemistry chemical physics : PCCP.

[16]  Dong-Sheng Cao,et al.  Recipe for revealing informative metabolites based on model population analysis , 2010, Metabolomics.

[17]  Yizeng Liang,et al.  GC–MS Based Serum Metabolomic Analysis of Isoflurane-Induced Postoperative Cognitive Dysfunctional Rats: Biomarker Screening and Insight into Possible Pathogenesis , 2012, Chromatographia.

[18]  Sarel Steel,et al.  Variable Selection for Support Vector Machines , 2009, Commun. Stat. Simul. Comput..

[19]  I. Wilson,et al.  A metabonomic study of strain- and age-related differences in the Zucker rat. , 2007, Rapid communications in mass spectrometry : RCM.

[20]  Yizeng Liang,et al.  A novel tree kernel support vector machine classifier for modeling the relationship between bioactivity and molecular descriptors , 2013 .

[21]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[22]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[23]  D. Massart,et al.  Application of Radial Basis Functions — Partial Least Squares to non-linear pattern recognition problems: diagnosis of process faults , 1996 .

[24]  Dong-Sheng Cao,et al.  Support Vector Machines and Their Application in Chemistry and Biotechnology , 2011 .

[25]  Roman M. Balabin,et al.  Support vector machine regression (SVR/LS-SVM)--an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data. , 2011, The Analyst.

[26]  Qing-Song Xu,et al.  Selective of informative metabolites using random forests based on model population analysis. , 2013, Talanta.

[27]  Roman M. Balabin,et al.  Near-infrared (NIR) spectroscopy for motor oil classification: From discriminant analysis to support vector machines , 2011 .

[28]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.