Detecting a difference – assessing generalisability when modelling metabolome fingerprint data in longer term studies of genetically modified plants
暂无分享,去创建一个
[1] Joshua D. Knowles,et al. Closed-loop, multiobjective optimization of two-dimensional gas chromatography/mass spectrometry for serum metabolomics. , 2007, Analytical chemistry.
[2] Douglas B. Kell,et al. Statistical strategies for avoiding false discoveries in metabolomics and related experiments , 2007, Metabolomics.
[3] John Draper,et al. Predicting interpretability of metabolome models based on behavior, putative identity, and biological relevance of explanatory signals , 2006, Proceedings of the National Academy of Sciences.
[4] John Draper,et al. On the Interpretation of High Throughput MS Based Metabolomics Fingerprints with Random Forest , 2006, CompLife.
[5] M. Taylor,et al. Assessing the potential for unintended effects in genetically modified potatoes perturbed in metabolic and developmental processes. Targeted analysis of key nutrients and anti-nutrients , 2006, Transgenic Research.
[6] Joachim Kopka,et al. Current challenges and developments in GC-MS based metabolite profiling technology. , 2006, Journal of biotechnology.
[7] U. Roessner,et al. Comprehensive metabolic profiling and phenotyping of interspecific introgression lines for tomato improvement , 2006, Nature Biotechnology.
[8] Thomas Lengauer,et al. ROCR: visualizing classifier performance in R , 2005, Bioinform..
[9] Nigel W. Hardy,et al. Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[10] W. Dunn,et al. Measuring the metabolome: current analytical technologies. , 2005, The Analyst.
[11] Yves Gibon,et al. GMD@CSB.DB: the Golm Metabolome Database , 2005, Bioinform..
[12] Royston Goodacre,et al. Genetic algorithm optimization for pre-processing and variable selection of spectroscopic data , 2005, Bioinform..
[13] R. Goodacre. Making sense of the metabolome using evolutionary computation: seeing the wood with the trees. , 2004, Journal of experimental botany.
[14] D. Fell. Enzymes, metabolites and fluxes. , 2004, Journal of experimental botany.
[15] Carlos E. Thomaz,et al. Using a Maximum Uncertainty LDA-Based Approach to Classify and Analyse MR Brain Images , 2004, MICCAI.
[16] Kazuki Saito,et al. Potential of metabolomics as a functional genomics tool. , 2004, Trends in plant science.
[17] J. Smedsgaard,et al. A new matching algorithm for high resolution mass spectra , 2004, Journal of the American Society for Mass Spectrometry.
[18] W. Weckwerth,et al. Metabolite profiling in plant biology: platforms and destinations , 2004, Genome Biology.
[19] G. Izmirlian,et al. Application of the Random Forest Classification Algorithm to a SELDI‐TOF Proteomics Study in the Setting of a Cancer Prevention Trial , 2004, Annals of the New York Academy of Sciences.
[20] D. Kell,et al. Metabolomics by numbers: acquiring and understanding global metabolite data. , 2004, Trends in biotechnology.
[21] Douglas B. Kell,et al. High-Throughput Metabolic Fingerprinting of Legume Silage Fermentations via Fourier Transform Infrared Spectroscopy and Chemometrics , 2004, Applied and Environmental Microbiology.
[22] Mariusz Kowalczyk,et al. A strategy for identifying differences in large series of metabolomic samples analyzed by GC/MS. , 2004, Analytical chemistry.
[23] Royston Goodacre,et al. Application of high-throughput Fourier-transform infrared spectroscopy in toxicology studies: contribution to a study on the development of an animal model for idiosyncratic toxicity. , 2004, Toxicology letters.
[24] Sameer Singh,et al. Multiresolution Estimates of Classification Complexity , 2003, IEEE Trans. Pattern Anal. Mach. Intell..
[25] M. Viant. Improved methods for the acquisition and interpretation of NMR metabolomic data. , 2003, Biochemical and biophysical research communications.
[26] Esther J Kok,et al. Comparative safety assessment for biotech crops. , 2003, Trends in biotechnology.
[27] Leo Breiman,et al. Two-Eyed Algorithms and Problems , 2003, PKDD.
[28] David Ward,et al. Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data , 2003, Bioinform..
[29] Richard Baumgartner,et al. Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions , 2003, Bioinform..
[30] D. Kell,et al. High-throughput classification of yeast mutants for functional genomics using metabolic footprinting , 2003, Nature Biotechnology.
[31] H. Kuiper,et al. Exploitation of molecular profiling techniques for GM food safety assessment. , 2003, Current opinion in biotechnology.
[32] Marianne Defernez,et al. Factors affecting the robustness of metabolite fingerprinting using 1H NMR spectra. , 2003, Phytochemistry.
[33] R. Dixon,et al. Plant metabolomics: large-scale phytochemistry in the functional genomics era. , 2003, Phytochemistry.
[34] Esther J Kok,et al. Substantial equivalence--an appropriate paradigm for the safety assessment of genetically modified foods? , 2002, Toxicology.
[35] D. Kell,et al. Metabolic profiling using direct infusion electrospray ionisation mass spectrometry for the characterisation of olive oils. , 2002, The Analyst.
[36] Ø. Langsrud,et al. 50–50 multivariate analysis of variance for collinear responses , 2002 .
[37] R. Takors,et al. Metabolomics: quantification of intracellular metabolite dynamics. , 2002, Biomolecular engineering.
[38] N. Reo. NMR-BASED METABOLOMICS , 2002, Drug and chemical toxicology.
[39] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[40] Leo Breiman,et al. Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001 .
[41] D B Kell,et al. Genomic computing. Explanatory analysis of plant expression profiling data using machine learning. , 2001, Plant physiology.
[42] U. Roessner,et al. Metabolic Profiling Allows Comprehensive Phenotyping of Genetically or Environmentally Modified Plant Systems , 2001, Plant Cell.
[43] O. Fiehn,et al. Metabolite profiling for plant functional genomics , 2000, Nature Biotechnology.
[44] L. Willmitzer,et al. Transgenic potato (Solanum tuberosum) tubers synthesize the full spectrum of inulin molecules naturally occurring in globe artichoke (Cynara scolymus) roots. , 2000, Proceedings of the National Academy of Sciences of the United States of America.
[45] Ute Roessner,et al. Simultaneous analysis of metabolites in potato tuber by gas chromatography-mass spectrometry. , 2000 .
[46] Thomas G. Dietterich. Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms , 1998, Neural Computation.
[47] David A. Fell,et al. Increasing the flux in metabolic pathways: A metabolic control analysis perspective , 1998, Biotechnology and bioengineering.
[48] L. Willmitzer,et al. Transgenic potato tubers accumulate high levels of 1-kestose and nystose: functional identification of a sucrose sucrose 1-fructosyltransferase of artichoke (Cynara scolymus) blossom discs. , 1997, The Plant journal : for cell and molecular biology.
[49] R. Tibshirani,et al. Improvements on Cross-Validation: The 632+ Bootstrap Method , 1997 .
[50] P. Good,et al. Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses , 1995 .
[51] S. Lohr. Statistics (2nd Ed.) , 1994 .
[52] D. Fell. Metabolic control analysis: a survey of its theoretical and experimental development. , 1992, The Biochemical journal.
[53] Chester Hartman,et al. Rejoinder by the Author , 1965 .
[54] O. Fiehn. Metabolomics – the link between genotypes and phenotypes , 2004, Plant Molecular Biology.
[55] Leo Breiman,et al. Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001, Statistical Science.
[56] U. Roessner,et al. Technical advance: simultaneous analysis of metabolites in potato tuber by gas chromatography-mass spectrometry. , 2000, The Plant journal : for cell and molecular biology.