标题： 通过交叉验证校正微阵列数据分类中的选择偏差 显示英文标题

标题： Correcting for selection bias via cross-validation in the classification of microarray data

摘要： 对基于微阵列数据的诊断规则的使用越来越感兴趣。 这些规则是通过考虑来自已知分类的患者组织样本中数千个基因的表达水平来形成的，这些分类代表了例如疾病状态或治疗策略。 由于这些规则的最终版本通常基于可用基因的一个小子集，因此在估计相关错误率时必须纠正这种选择偏差。 我们使用交叉验证来考虑这个问题。 特别是，我们提出了有用的显式公式，这些公式有助于解释为了避免不正确交叉验证的估计而必须执行的验证层次。 显示英文摘要

摘要： There is increasing interest in the use of diagnostic rules based on microarray data. These rules are formed by considering the expression levels of thousands of genes in tissue samples taken on patients of known classification with respect to a number of classes, representing, say, disease status or treatment strategy. As the final versions of these rules are usually based on a small subset of the available genes, there is a selection bias that has to be corrected for in the estimation of the associated error rates. We consider the problem using cross-validation. In particular, we present explicit formulae that are useful in explaining the layers of validation that have to be performed in order to avoid improperly cross-validated estimates.