判别微阵列数据分析需要多少基因？
How Many Genes Are Needed for a Discriminant Microarray Data Analysis ?
摘要：
来自Whitehead/MIT小组的白血病数据的分析是判别分析（也称为监督学习）。在成千上万的表达水平被测量的基因中，并非所有基因都需要进行判别分析：一个基因可能对两种类型的组织/癌症的分离没有贡献，或者它可能是多余的，因为它与其他基因高度相关。有两种理论框架可以解决变量选择（或基因选择）。第一个是模型选择，第二个是模型平均。我们使用赤池信息标准和贝叶斯信息标准进行了模型选择，并进行了逻辑回归（判别、预测或分类），以确定提供最佳模型的基因数量。这些模型选择标准为38个样本的数据集设定了22-25个和12-13个基因的上限，并且最佳模型仅由一个（no.4847，zyxin）或两个基因组成。我们还使用三种不同的权重对最佳单基因逻辑预测因子进行了模型平均：最大似然、训练集预测率和等权重。我们已经观察到，随着包含更多基因，大多数这些加权预测因子在测试集上的性能逐渐降低，但是没有发现区分好的和坏的预测性能的明确截止值。
英文摘要：
The analysis of the leukemia data from Whitehead/MIT group is a discriminant analysis (also called a supervised learning). Among thousands of genes whose expression levels are measured, not all are needed for discriminant analysis: a gene may either not contribute to the separation of two types of tissues/cancers, or it may be redundant because it is highly correlated with other genes. There are two theoretical frameworks in which variable selection (or gene selection in our case) can be addressed. The first is model selection, and the second is model averaging. We have carried out model selection using Akaike information criterion and Bayesian information criterion with logistic regression (discrimination, prediction, or classification) to determine the number of genes that provide the best model. These model selection criteria set upper limits of 22-25 and 12-13 genes for this data set with 38 samples, and the best model consists of only one (no.4847, zyxin) or two genes. We have also carried out model averaging over the best single-gene logistic predictors using three different weights: maximized likelihood, prediction rate on training set, and equal weight. We have observed that the performance of most of these weighted predictors on the testing set is gradually reduced as more genes are included, but a clear cutoff that separates good and bad prediction performance is not found.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
