A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis
A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis
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DOI：
10.1093/bioinformatics/bti033
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发表时间：
2005-03-01
期刊：
影响因子：
5.8
通讯作者：
Levy, S
中科院分区：
文献类型：
--
作者：
Statnikov, A;Aliferis, CF;Levy, S
研究动机：癌症诊断是基因表达微阵列技术最重要的临床应用之一。我们正在寻求开发一种计算机系统，用于基于微阵列数据创建强大而可靠的癌症诊断模型。为了保持对临床应用的现实看法，我们专注于多类别诊断。为了使系统具有分类器、基因选择和交叉验证方法的最佳组合，我们使用涵盖74个诊断类别、41个癌症类型和12个正常组织类型的11个数据集，对几种主要的多类别分类算法、几种基因选择方法、多个集成分类器方法和两个交叉验证设计进行了系统和全面的评估。结果：多类别支持向量机(MC-SVMs)是从基因表达数据中进行准确癌症诊断的最有效的分类器。Crammer和Singer、Weston和Watkins提出的MC-支持向量机技术和一对多支持向量机是这一领域中最好的方法。MC-支持向量机的性能优于其他流行的机器学习算法，如k近邻、反向传播和概率神经网络，通常在很大程度上优于这些算法。基因选择技术可以显著提高MC-支持向量机和其他非支持向量机学习算法的分类性能。集成分类器通常不会改善最佳非集成模型的性能。这些结果指导了软件系统GEMS(基因表达模型选择器)的构建，该系统可以自动构建高质量的模型，并执行合理的优化和性能评估程序。这是第一个通过对可用的算法和数据集进行严格的比较分析而获得信息的此类系统。
Motivation: Cancer diagnosis is one of the most important emerging clinical applications of gene expression microarray technology. We are seeking to develop a computer system for powerful and reliable cancer diagnostic model creation based on microarray data. To keep a realistic perspective on clinical applications we focus on multicategory diagnosis. To equip the system with the optimum combination of classifier, gene selection and cross-validation methods, we performed a systematic and comprehensive evaluation of several major algorithms for multicategory classification, several gene selection methods, multiple ensemble classifier methods and two cross-validation designs using 11 datasets spanning 74 diagnostic categories and 41 cancer types and 12 normal tissue types.Results: Multicategory support vector machines (MC-SVMs) are the most effective classifiers in performing accurate cancer diagnosis from gene expression data. The MC-SVM techniques by Crammer and Singer, Weston and Watkins and one-versus-rest were found to be the best methods in this domain. MC-SVMs outperform other popular machine learning algorithms, such as k-nearest neighbors, backpropagation and probabilistic neural networks, often to a remarkable degree. Gene selection techniques can significantly improve the classification performance of both MC-SVMs and other non-SVM learning algorithms. Ensemble classifiers do not generally improve performance of the best non-ensemble models. These results guided the construction of a software system GEMS (Gene Expression Model Selector) that automates high-quality model construction and enforces sound optimization and performance estimation procedures. This is the first such system to be informed by a rigorous comparative analysis of the available algorithms and datasets.