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Chemometrics in Metabonomics and Metabolomics |
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Written by Johan Trygg, Umeå University & Torbjörn Lundstedt, AcurePharma
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Monday, 05 March 2007 |
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In the post-genomics era, the use of methodologies that enable transcriptomic,proteomic and metabolomic data to be analysed in detail have revolutionized biological investigations. One of the major advantages with metabolomics investigations compared to traditional target metabolite analysis is that metabolomics data can give an unbiased view of changes in metabolism during environmental,genetic or developmental changes. Instead of tracking only a few metabolites, changes in relative amounts in 300 to 1000 or even more metabolites can be recorded and analysed, covering all major metabolic pathways.
This development has accentuated the need to apply and further develop chemometric methodology. |
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Last Updated ( Monday, 05 March 2007 )
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The Use of Virtual Calibrations to Facilitate Understanding of Factor Analysis |
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Written by Jonas Schenk, Michal Dabros, Ian W. Marison* and Urs von Stockar
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Monday, 05 March 2007 |
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Factor Analysis (FA), which includes Principal Component Analysis (PCA) and Partial Least Squares (PLS), is more and more employed in academia and industry for various purposes such as spectrometer calibration, process modeling, data mining, quality control, etc. While software offering friendly interfaces have contributed to make this approach extremely popular, FA remains far from being straightforward, and examples of inappropriate use are not rare. |
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Last Updated ( Saturday, 08 November 2008 )
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Multivariate prediction uncertainty |
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Written by Klaas Faaber, Chemometry Consultancy
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Saturday, 02 September 2006 |
Calibration models are at best (local) approximations, and so are the predictions based on such a model. This is a fact that does not depend on the complexity of the input data, i.e. it should not matter whether the input data are univariate or multivariate. Model–based results such as predictions must therefore be reported together with an estimate of their uncertainty.
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Last Updated ( Friday, 29 September 2006 )
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Support Vector Machines and interpretation |
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Written by Ravi Mallela, Equibits
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Saturday, 02 September 2006 |
SVM’s are a relatively new technique that is being applied to QPSR Modeling. One of the major criticisms of SVM’s have been that they are “black boxes” that provide uninterpretable results. In reality, SVM’s lend themselves to easily interpretable models and results.
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Last Updated ( Friday, 29 September 2006 )
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Classification with Support Vector Machines |
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Written by Simeone Zomer
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Saturday, 02 September 2006 |
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Classification is very common task in chemometrics and several methods are reported in the literature for solving this type of problems. The choice of the most appropriate method depends on the specific nature of the problem. |
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Last Updated ( Friday, 29 September 2006 )
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