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This work demonstrates the execution of a novel process model for knowledgediscovery and data mining for metabolomics (MeKDDaM). It aims to illustrateMeKDDaM process model applicability using four different real-worldapplications and to highlight its strengths and unique features. Thedemonstrated applications provide coverage for metabolite profiling, targetanalysis, and metabolic fingerprinting. The data analysed in these applicationswere captured by chromatographic separation and mass spectrometry technique(LC-MS), Fourier transform infrared spectroscopy (FT-IR), and nuclear magneticresonance spectroscopy (NMR) and involve the analysis of plant, animal, andhuman samples. The process was executed using both data-driven andhypothesis-driven data mining approaches in order to perform various datamining goals and tasks by applying a number of data mining techniques. Theapplications were selected to achieve a range of analytical goals and researchquestions and to provide coverage for metabolite profiling, target analysis,and metabolic fingerprinting using datasets that were captured by NMR, LC-MS,and FT-IR using samples of a plant, animal, and human origin. The process wasapplied using an implementation environment which was created in order toprovide a computer-aided realisation of the process model execution.

Applications of a Novel Knowledge Discovery and Data Mining Process Model for Metabolomics