An intensity-region driven multi-classifier scheme for improving the classification accuracy of proteomic MS-spectra
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- 作者：Panagiotis Bougioukos ; Dimitris Glotsos ; Dionisis Cavouras ; Antonis Daskalakis ; Ioannis Kalatzis ; Spiros Kostopoulos ; George Nikiforidis ; Anastasios Bezerianos
- 关键词：Classification ; Ovarian cancer ; Pre-processing
- 刊名：Computer Methods and Programs in Biomedicine
- 出版年：2010
- 出版时间：August 2010
- 年：2010
- 卷：99
- 期：2
- 页码：147-153
- 全文大小：387 K
文摘
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.