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

Panagiotis Bougioukos, Dimitris Glotsos, Dionisis Cavouras, Antonis Daskalakis, Ioannis Kalatzis, Spiros Kostopoulos, George Nikiforidis, Anastasios Bezerianos

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)147-153
Number of pages7
JournalComputer Methods and Programs in Biomedicine
Volume99
Issue number2
DOIs
StatePublished - Aug 1 2010

Keywords

  • Classification
  • Ovarian cancer
  • Pre-processing

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Health Informatics

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