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Serum peptide and protein profiling studies are widely employed in biomarker discovery studies. In MS-based clinical proteomics, peptide- or protein levels in serum of healthy and diseased individuals are mapped in a single spectrum, aiming for identifying differences. The signature of biomarker candidates that is found through proteomics studies holds great promise for personalized medicine. Multiple data handling strategies have been reported for the processing and statistical analysis of peptide- and protein profiles, either model-based or applying different feature selection strategies. Initially, feature selection was based on simple binning procedures or finding local maxima. The accuracy of Mass Spectrometry (MS)-based analysis of peptides in complex biological mixtures improves upon using high resolution instrumentation. However, high resolution content poses challenges to data processing and statistical analysis. Here, three different data handling strategies were evaluated with respect to classification performance using a well-defined cohort of serum samples from Duchenne Muscular Dystrophy (DMD) patients and controls. For this purpose, serum samples were purified using a solid-phase extraction (SPE) protocol based on Reversed-Phase (RP) C18 magnetic beads. Isotopically-resolved peptide profiles were acquired on a Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) mass spectrometer and examined by either using the full mass spectrum or after selecting peaks between 1000