Ghosh Debashis
Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, Michigan 48105, USA.
Biometrics. 2003 Dec;59(4):992-1000. doi: 10.1111/j.0006-341x.2003.00114.x.
Due to the advent of high-throughput microarray technology, it has become possible to develop molecular classification systems for various types of cancer. In this article, we propose a methodology using regularized regression models for the classification of tumors in microarray experiments. The performances of principal components, partial least squares, and ridge regression models are studied; these regression procedures are adapted to the classification setting using the optimal scoring algorithm. We also develop a procedure for ranking genes based on the fitted regression models. The proposed methodologies are applied to two microarray studies in cancer.
由于高通量微阵列技术的出现，开发针对各种癌症类型的分子分类系统已成为可能。在本文中，我们提出了一种使用正则化回归模型在微阵列实验中对肿瘤进行分类的方法。研究了主成分、偏最小二乘法和岭回归模型的性能；这些回归程序通过最优评分算法适用于分类设置。我们还开发了一种基于拟合回归模型对基因进行排名的程序。所提出的方法应用于两项癌症微阵列研究。
