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NMR Metabonomics for the Detection of Anabolic Steroids: Development of Statistical Methods
A combination of nuclear magnetic resonance (NMR) together with chemometrics can be used for metabolic profiling and interpreting metabolic fingerprints in different biological systems. Amongst others, statistical correlation spectroscopy will be employed to identify biomarkers.
Keywords: Chemometrics, nuclear magnetic resonance, statistical treatment of spectroscopic data, metabolomics, biomarkers, Multivariate Design, Statistical Spectroscopy, metabolite profiling
Identification of NMR spectroscopic signals using statistical methods is an important aspect in the proposed study. For this purpose different types of software will be applied. You will learn statistical methods commonly used in chemometrics and the latest methods designed to improve the statistical signal elucidation in their application in NMR spectroscopy. Interested candidates should either provide some background in the application of software like Matlab, Excel, Origin and feel comfortable to rapidly acquire new software skills and become acquainted with statistical methods. The project will be exclusively reserved to statistical data treatment, no laboratory experience is required. You should be interested in working with modern software for data evaluation, background in chemistry/biochemistry is a plus, but not required.
Identification of NMR spectroscopic signals using statistical methods is an important aspect in the proposed study. For this purpose different types of software will be applied. You will learn statistical methods commonly used in chemometrics and the latest methods designed to improve the statistical signal elucidation in their application in NMR spectroscopy. Interested candidates should either provide some background in the application of software like Matlab, Excel, Origin and feel comfortable to rapidly acquire new software skills and become acquainted with statistical methods. The project will be exclusively reserved to statistical data treatment, no laboratory experience is required. You should be interested in working with modern software for data evaluation, background in chemistry/biochemistry is a plus, but not required.
The goals of this project include
- to implement and test already existing statistic codes (Matlab, Excel) to discriminate between treated and control samples
- to design new scripts for better understanding the metabolic profile of testosterone intake and hopefully to be able to differentiate between testosterone and its metabolites and endogenous metabolites.
The goals of this project include
- to implement and test already existing statistic codes (Matlab, Excel) to discriminate between treated and control samples
- to design new scripts for better understanding the metabolic profile of testosterone intake and hopefully to be able to differentiate between testosterone and its metabolites and endogenous metabolites.
Dr. Elena Voronina (Elena.Voronina@uni-koeln.de),
AG Schloerer (NMR-Abteilung, Department fuer Chemie, Universitaet zu Koeln)
Dr. Elena Voronina (Elena.Voronina@uni-koeln.de), AG Schloerer (NMR-Abteilung, Department fuer Chemie, Universitaet zu Koeln)