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首页> 外文会议>Australian Joint Conference on Artificial Intelligence; 20071202-06; Gold Coast(AU) >Bagging Support Vector Machine for Classification of SELDI-ToF Mass Spectra of Ovarian Cancer Serum Samples
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Bagging Support Vector Machine for Classification of SELDI-ToF Mass Spectra of Ovarian Cancer Serum Samples

机译：用于卵巢癌血清样品SELDI-ToF质谱分类的装袋支持向量机

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摘要

There has been much progresses recently about the identification of diagnostic proteomic signatures for different human cancers using surface-enhanced laser desorption ionization time-of-flight (SELDI-TOF) mass spectrometry. To identify proteomic patterns in serum to discriminate cancer patients from normal individuals, many classification methods have been experimented, often with successful results. Most of these earlier studies, however, are based on the direct application of original mass spectra, together with dimension reduction methods like PCA or feature selection methods like T-tests. Because only the peaks of MS data correspond to potential biomarkers, it is important to study classification methods using the detected peaks. This paper investigates ovarian cancer identification from the detected MS peaks by applying Bagging Support Vector Machine as a special strategy of bootstrap aggregating (Bagging). In bagging SVM, each individual SVM is trained independently, using randomly chosen training samples via a bootstrap technique. The trained individual SVMs are aggregated to make a collective decision in an appropriate way, for example, the majority voting. Bagged SVM demonstrated a 94% accuracy with 95% sensitivity and 92% specificity respectively by using the detected peaks. The efficiency can be further improved by applying PCA to reduce the dimension.
机译：最近，关于使用表面增强激光解吸电离飞行时间（SELDI-TOF）质谱法鉴定不同人类癌症的诊断蛋白质组学特征方面取得了许多进展。为了鉴定血清中的蛋白质组模式以将癌症患者与正常人区分开，已经尝试了许多分类方法，通常会获得成功的结果。但是，这些早期研究中的大多数都是基于原始质谱图的直接应用，以及诸如PCA的降维方法或诸如T检验的特征选择方法。由于仅MS数据的峰对应于潜在的生物标志物，因此使用检测到的峰研究分类方法非常重要。本文应用Bagging支持向量机作为自举聚合（Bagging）的特殊策略，从检测到的MS峰中研究卵巢癌的鉴定。在袋装SVM中，通过引导技术使用随机选择的训练样本对每个单独的SVM进行独立训练。将经过培训的个人SVM汇总起来，以适当的方式（例如，多数投票）做出集体决策。装袋的SVM通过使用检测到的峰分别显示出94％的准确度，95％的灵敏度和92％的特异性。通过应用PCA减小尺寸，可以进一步提高效率。

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