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Showing 1 to 2 of 2 for “"Clinical metabolomics"”.
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Deriving statistical inference from the application of artificial neural networks to clinical metabolomics data
Metabolomics data are complex with a high degree of multicollinearity. As such, multivariate linear projection methods, such as partial least squares discriminant analysis (PLS-DA) have become standard. Non-linear projections methods, typified by Artificial Neural Networks (ANNs) may be more …
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Applications of metabolomics to study the pathophysiology of adverse pregnancy outcomes
Background: Clinical metabolomics is a growing field of research aiming to use metabolomic techniques to gain further knowledge into diseases, the use of biomarkers to predict their onset, or the effect of a potential therapeutic agent on the metabolome. Adverse pregnancy outcomes, such as small …