Measuring the metabolome: current analytical technologies.

By Warwick B. Dunn, N. J. Bailey, Helen E. Johnson. In Analysis, 2005

909 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/af1aa83b72cdc247d0a357c3582c18e3feeb1b4e

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Citations in the corpus

The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo (2006, neutral): “We expect such data sets to be available soon [45] for several organisms as part of ongoing functional genomics efforts, and we can foresee no major methodologic barriers to the use of such data in the framework described here.”
Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry (2011, methodology): “Metabolic profiling studies have been performed using a range of analytical platform”
Discovery and resupply of pharmacologically active plant-derived natural products: A review (2015, background): “The identification and quantification of secondary metabolites in plant extracts in general is highly challenging (Ebada et al., 2008; Kjer et al., 2010) due to the diversity of the chemical and physical properties and the wide range of concentrations of the analytes (Dunn et al., 2005).”
Genome-scale models of bacterial metabolism: reconstruction and applications (2008, background): “MS and nuclear magnetic resonance (NMR) techniques are able to identify and quantify large sets of metabolites at high throughput (Dunn et al ., 2005; Dettmer et al ., 2007).”
Organization of GC/MS and LC/MS metabolomics data into chemical libraries (2010, background): “, LC/MS, GC/MS), capillary electrophoresis (CE), and nuclear magnetic resonance (NMR) spectroscopy [4].”
Metabolomics for Investigating Physiological and Pathophysiological Processes. (2019, methodology): “Modern NMR and MS instruments are capable of separating, detecting, and characterizing hundreds to thousands of chemicals in complex chemical mixtures, such as biofluids or tissue extracts (127, 510).”
Clinical Applications of Metabolomics in Oncology: A Review (2009, methodology): “Because 1H-NMR or MS spectra from biofluids or tumor tissue contain hundreds of signals from endogenous metabolites and are highly redundant, spectral data sets, reduced to 100 to 500 spectral segments, and their respective signal intensities are directly entered into statistical programs (5,21,29).”