  This work demonstrates the execution of a novel process model for knowledge
discovery and data mining for metabolomics (MeKDDaM). It aims to illustrate
MeKDDaM process model applicability using four different real-world
applications and to highlight its strengths and unique features. The
demonstrated applications provide coverage for metabolite profiling, target
analysis, and metabolic fingerprinting. The data analysed in these applications
were captured by chromatographic separation and mass spectrometry technique
(LC-MS), Fourier transform infrared spectroscopy (FT-IR), and nuclear magnetic
resonance spectroscopy (NMR) and involve the analysis of plant, animal, and
human samples. The process was executed using both data-driven and
hypothesis-driven data mining approaches in order to perform various data
mining goals and tasks by applying a number of data mining techniques. The
applications were selected to achieve a range of analytical goals and research
questions 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 was
applied using an implementation environment which was created in order to
provide a computer-aided realisation of the process model execution.
