Background#R##N#One of the goals of global metabolomic analysis is to identify metabolic markers that are hidden within a large background of data originating from high-throughput analytical measurements. Metabolite-based clustering is an unsupervised approach for marker identification based on grouping similar concentration profiles of putative metabolites. A major problem of this approach is that in general there is no prior information about an adequate number of clusters.
['Peter Meinicke', 'Thomas Lingner', 'Alexander Kaever', 'Kirstin Feussner', 'Cornelia Göbel', 'Ivo Feussner', 'Petr Karlovsky', 'Burkhard Morgenstern']
Metabolite-based clustering and visualization of mass spectrometry data using one-dimensional self-organizing maps.