Department of Medical Physics, University of Patras, Rio, Greece.
Comput Methods Programs Biomed. 2010 Aug;99(2):147-53. doi: 10.1016/j.cmpb.2009.11.003. Epub 2009 Dec 9.
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.
在这项研究中，提出了一种模式识别系统，通过使用多数投票集成组合从不同的 MS 光谱强度区域收集信息，从而提高 MS 光谱的分类准确性。该方法首先自动将所有 MS 光谱分解为常见的强度区域。随后，从所有 MS 光谱的每个常见强度区域中提取最具信息量的特征（m/z 值），这些特征可能构成潜在的显著生物标志物，最后使用多分类器方案（支持向量机、概率神经网络和 k 最近邻分类器）对正常和卵巢癌 MS 光谱进行区分。临床材料取自公开的卵巢蛋白质组数据集（8-7-02）。为了确保稳健和可靠的估计，使用外部交叉验证过程评估了所提出的模式识别系统。该系统在区分正常和癌症卵巢 MS 光谱方面的平均整体性能为 97.18%，平均灵敏度为 98.52%，平均特异性为 94.84%。
