Tibshirani Robert, Hastie Trevor, Narasimhan Balasubramanian, Chu Gilbert
Department of Health, Research and Policy, and Statistics, Stanford University, Stanford, CA 94305, USA.
Proc Natl Acad Sci U S A. 2002 May 14;99(10):6567-72. doi: 10.1073/pnas.082099299.
We have devised an approach to cancer class prediction from gene expression profiling, based on an enhancement of the simple nearest prototype (centroid) classifier. We shrink the prototypes and hence obtain a classifier that is often more accurate than competing methods. Our method of "nearest shrunken centroids" identifies subsets of genes that best characterize each class. The technique is general and can be used in many other classification problems. To demonstrate its effectiveness, we show that the method was highly efficient in finding genes for classifying small round blue cell tumors and leukemias.
我们设计了一种基于简单最近原型(质心)分类器增强的从基因表达谱进行癌症类别预测的方法。我们收缩原型,从而获得一个通常比竞争方法更准确的分类器。我们的“最近收缩质心”方法可识别最能表征每个类别的基因子集。该技术具有通用性,可用于许多其他分类问题。为证明其有效性,我们表明该方法在寻找用于分类小细胞蓝细胞瘤和白血病的基因方面非常高效。