Data mining techniques for cancer detection using serum proteomic profiling
Data mining techniques for cancer detection using serum proteomic profiling
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DOI：
10.1016/j.artmed.2004.03.006
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发表时间：
2004-10-01
影响因子：
7.5
通讯作者：
Clark, RA
中科院分区：
文献类型：
--
作者：
Li, LH;Tang, H;Clark, RA
目的：血清蛋白质组学模式可以反映器官或组织的病理变化。独特的血清蛋白质组学模式可能用于区分癌症样本和非癌症样本。由于蛋白质组分析的复杂性，需要更高阶的分析，如数据挖掘，以揭示复杂的蛋白质组模式的差异。本文的目的是（1）简要回顾数据挖掘技术在蛋白质组学中用于癌症检测/诊断的应用;（2）探索具有不同特征选择方法的新的分析方法;（3）比较不同数据集上获得的结果和Petricoin等人报道的结果，在检测性能和选择的蛋白质组模式方面。方法和材料：在这项研究中使用了三个血清SELDI MS数据集，以确定血清蛋白质组模式，区分卵巢癌病例的血清非癌症对照。本文提出了一种基于支持向量机的特征选择方法，分别采用统计检验和遗传算法进行特征选择。留一交叉验证与受试者工作特征（ROC）曲线用于评估和比较癌症检测性能。结果和结论：结果表明：（1）数据挖掘技术可以成功地应用于卵巢癌检测，具有相当高的性能;（2）使用遗传算法选择的特征的分类在准确性和鲁棒性方面始终优于统计测试选择的特征;（3）区分特征（蛋白质组模式）可以从一种选择方法到另一种选择方法非常不同。换句话说，模式选择及其分类效率是高度依赖于分类器的。因此，当使用数据挖掘技术时，癌症与正常的区别不仅仅取决于癌症相关蛋白的身份和起源。(C)2004 Elsevier B. V.保留所有权利。
Objective: Pathological changes in an organ or tissue may be reflected in proteomic patterns in serum. It is possible that unique serum proteomic patterns could be used to discriminate cancer samples from non-cancer ones. Due to the complexity of proteomic profiling, a higher order analysis such as data mining is needed to uncover the differences in complex proteomic patterns. The objectives of this paper are (1) to briefly review the application of data mining techniques in proteomics for cancer detection/diagnosis; (2) to explore a novel analytic method with different feature selection methods; (3) to compare the results obtained on different datasets and that reported by Petricoin et al. in terms of detection performance and selected proteomic patterns. Methods and material: Three serum SELDI MS data sets were used in this research to identify serum proteomic patterns that distinguish the serum of ovarian cancer cases from non-cancer controls. A support vector machine-based method is applied in this study, in which statistical testing and genetic algorithm-based methods are used for feature selection respectively. Leave-one-out cross validation with receiver operating characteristic (ROC) curve is used for evaluation and comparison of cancer detection performance. Results and conclusions: The results showed that (1) data mining techniques can be successfully applied to ovarian cancer detection with a reasonably high performance; (2) the classification using features selected by the genetic algorithm consistently outperformed those selected by statistical testing in terms of accuracy and robustness; (3) the discriminatory features (proteomic patterns) can be very different from one selection method to another. In other words, the pattern selection and its classification efficiency are highly classifier dependent. Therefore, when using data mining techniques, the discrimination of cancer from normal does not depend solely upon the identity and origination of cancer-related proteins. (C) 2004 Elsevier B.V. All rights reserved.