An intensity-region driven multi-classifier scheme for improving the classification accuracy of proteomic MS-spectra.
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.
Bougioukos
Panagiotis
P
Department of Medical Physics, University of Patras, Rio, Greece. bougiouk@upatras.gr
Glotsos
Dimitris
D
Cavouras
Dionisis
D
Daskalakis
Antonis
A
Kalatzis
Ioannis
I
Kostopoulos
Spiros
S
Nikiforidis
George
G
Bezerianos
Anastasios
A
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*
D015415:Biomarkers