biostudies-literatureUnknown3(1)Silvestre DD<h4>Unlabelled</h4><h4>Background</h4>Mass spectrometry is an important analytical tool for clinical proteomics. Primarily employed for biomarker discovery, it is increasingly used for developing methods which may help to provide unambiguous diagnosis of biological samples. In this context, we investigated the classification of phenotypes by applying support vector machine (SVM) on experimental data obtained by MudPIT approach. In particular, we compared the performance capabilities of SVM by using two independent collection of complex samples and different data-types, such as mass spectra (m/z), peptides and proteins.<h4>Results</h4>Globally, protein and peptide data allowed a better discriminant informative content than experimental mass spectra (overall accuracy higher than 87% in both Journal of clinical bioinformatics1https://www.ebi.ac.uk/biostudies/studies/S-EPMC3563498biostudies-literatureAvailability of MudPIT data for classification of biological samples.PMC3563498Silvestre DDMauri GMauri PBrambilla FBellettato VZoppis IfalseAvailability of MudPIT data for classification of biological samples.<h4>Unlabelled</h4><h4>Background</h4>Mass spectrometry is an important analytical tool for clinical proteomics. Primarily employed for biomarker discovery, it is increasingly used for developing methods which may help to provide unambiguous diagnosis of biological samples. In this context, we investigated the classification of phenotypes by applying support vector machine (SVM) on experimental data obtained by MudPIT approach. In particular, we compared the performance capabilities of SVM by using two independent collection of complex samples and different data-types, such as mass spectra (m/z), peptides and proteins.<h4>Results</h4>Globally, protein and peptide data allowed a better discriminant informative content than experimental mass spectra (overall accuracy higher than 87% in both 2013-01-01T00:00:00Z2013 Jan2025-04-21T21:41:17.42Z2019-03-27T01:04:18ZS-EPMC35634982331745510.1186/2043-9113-3-1