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 are 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.