An intensity-region driven multi-classifier scheme for improving the classification accuracy of proteomic MS-spectra

Authors:
Panagiotis Bougioukos;Dimitris Glotsos;Dionisis Cavouras;Antonis Daskalakis;Ioannis Kalatzis;Spiros Kostopoulos;George Nikiforidis;Anastasios Bezerianos
Affiliations:
Department of Medical Physics, School of Medicine, University of Patras, GR-26504 Patras, Rio, Greece;Medical Signal and Image Processing Lab, Department of Medical Instruments Technology, Technological Educational Institute of Athens, Greece;Medical Signal and Image Processing Lab, Department of Medical Instruments Technology, Technological Educational Institute of Athens, Greece;Department of Medical Physics, School of Medicine, University of Patras, GR-26504 Patras, Rio, Greece;Medical Signal and Image Processing Lab, Department of Medical Instruments Technology, Technological Educational Institute of Athens, Greece;Department of Medical Physics, School of Medicine, University of Patras, GR-26504 Patras, Rio, Greece;Department of Medical Physics, School of Medicine, University of Patras, GR-26504 Patras, Rio, Greece;Department of Medical Physics, School of Medicine, University of Patras, GR-26504 Patras, Rio, Greece
Venue:
Computer Methods and Programs in Biomedicine
Year:
2010

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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.