title: An intensity-region driven multi-classifier scheme for improving the classification accuracy of proteomic MS-spectra.
abstract: In this study, a pattern recognition system is presented for improving the classification accuracy of MS-spectra by means of gathering information from different MS-spectra intensity regions using a majority vote ensemble combination. The method starts by automatically breaking down all MS-spectra into common intensity regions. Subsequently, the most informative features (m/z values), which might constitute potential significant biomarkers, are extracted from each common intensity region over all the MS-spectra and, finally, normal from ovarian cancer MS-spectra are discriminated using a multi-classifier scheme, with members the Support Vector Machine, the Probabilistic Neural Network and the k-Nearest Neighbour classifiers. Clinical material was obtained from the publicly available ovarian proteomic dataset (8-7-02). To ensure robust and reliable estimates, the proposed pattern recognition system was evaluated using an external cross-validation process. The average overall performance of the system in discriminating normal from cancer ovarian MS-spectra was 97.18% with 98.52% mean sensitivity and 94.84% mean specificity values.
authors.affiliation: Department of Medical Physics, University of Patras, Rio, Greece. bougiouk@upatras.gr
authors.forename: Panagiotis
authors.initials: P
authors.lastname: Bougioukos
authors.forename: Dimitris
authors.initials: D
authors.lastname: Glotsos
authors.forename: Dionisis
authors.initials: D
authors.lastname: Cavouras
authors.forename: Antonis
authors.initials: A
authors.lastname: Daskalakis
authors.forename: Ioannis
authors.initials: I
authors.lastname: Kalatzis
authors.forename: Spiros
authors.initials: S
authors.lastname: Kostopoulos
authors.forename: George
authors.initials: G
authors.lastname: Nikiforidis
authors.forename: Anastasios
authors.initials: A
authors.lastname: Bezerianos
mesh_terms: D015415:Biomarkers / Q000032:analysis; D016208:Databases, Factual; D005260:Female; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D016571:Neural Networks, Computer; D010051:Ovarian Neoplasms / Q000378:metabolism; D040901:Proteomics / Q000379:methods*
chemical_list: D015415:Biomarkers