Discrimination models using variance-stabilizing transformation of metabolomic NMR data.

After the extensive work that is being done in the areas of genomics, proteomics, and metabolomics, the study of metabolites has come of interest in its own right. Metabolites in biological systems give an understanding of the state of the system and provide a powerful tool for the study of disease and other maladies. Several analytical techniques such as mass spectrometry and high-resolution NMR spectroscopy have been used to study metabolites.

Discriminant models for high-throughput proteomics mass spectrometer data.

We use several different multivariate analysis methods to discriminate between diseased and healthy patients using protein mass spectrometer data provided by Duke University. Two problems were presented by the university; one in which the responses (diseased or healthy) of the patients were not known and second, when the responses were known. In the latter case, the data can be used as a 'training' set.