Classification of gene microarrays by penalized logistic regression
Classification of gene microarrays by penalized logistic regression
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DOI：
10.1093/biostatistics/kxg046
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发表时间：
2004-07-01
期刊：
影响因子：
2.1
通讯作者：
Hastie, T
中科院分区：
文献类型：
--
作者：
Zhu, J;Hastie, T
患者样本的分类是癌症诊断和治疗的重要方面。支持向量机（SVM）已成功应用于微阵列癌症诊断问题。然而，SVM的一个弱点是，给定肿瘤样本，它只能预测癌症类别标签，但不能提供潜在概率的任何估计。我们提出惩罚逻辑回归（PLR）作为替代的支持向量机的微阵列癌症诊断问题。我们表明，当使用相同的基因集时，PLR和SVM在癌症分类中的表现相似，但PLR具有额外提供潜在概率估计的优势。通常，微阵列癌症诊断的主要目标是识别负责分类的基因，而不是类别预测。本文考虑了两种基因选择方法，单变量排序（UR）和递归特征消除（RFE）。实验结果表明，PLR与RFE相结合，往往选择更少的基因比其他方法，也表现出良好的交叉验证和测试样本。文中还介绍了一种求解PLR的快速算法。
Classification of patient samples is an important aspect of cancer diagnosis and treatment. The support vector machine (SVM) has been successfully applied to microarray cancer diagnosis problems. However, one weakness of the SVM is that given a tumor sample, it only predicts a cancer class label but does not provide any estimate of the underlying probability. We propose penalized logistic regression (PLR) as an alternative to the SVM for the microarray cancer diagnosis problem. We show that when using the same set of genes, PLR and the SVM perform similarly in cancer classification, but PLR has the advantage of additionally providing an estimate of the underlying probability. Often a primary goal in microarray cancer diagnosis is to identify the genes responsible for the classification, rather than class prediction. We consider two gene selection methods in this paper, univariate ranking (UR) and recursive feature elimination (RFE). Empirical results indicate that PLR combined with RFE tends to select fewer genes than other methods and also performs well in both cross-validation and test samples. A fast algorithm for solving PLR is also described.