SD Class Prediction by Gene Expression Profiles

DNA微阵列提供了一种非常有效的方法来同时监测数千个基因的表达水平。在基因芯片数据分析中，类别预测是一项至关重要的任务。分类预测的目的是根据样本的基因表达谱对其诊断类别进行分类和预测。这个项目研究通过基因表达谱预测班级，包括四个主要目标。首先，提出了一种新的选择基因的统计方法。其次，提出了用随机判别法(SD)来构造类预测器。SD的原理是，它首先生成许多弱规则，每个规则都可以作为一种分类方法，通常分类错误很大，然后以一种方式将这些“弱规则”组合在一起，形成一个分类错误率较低的强分类器。利用两类技术建立多类SD预报器。将对SD预报器的预测精度的理论结果进行研究。本课程将探讨SD预报器的几何性质。相信这些结果不仅将显着提高对预测因子的认识，也将为预测因子的应用提供重要的理论支撑。第三，提出了一种新的比较不同类别预测器的方法。它将解决预测者的真实准确性是否因统计而有显著差异，如果这种差异显著，这种差异的范围是什么。第四，对所提出的研究工作进行了广泛的评估，通过模拟和将预测器应用于真实的基因表达数据集，并将SD预测器的性能与其他竞争算法的性能进行了比较

DNA microarrays provide a very effective approach to monitor expression levels of thousands of genes simultaneously. In microarray data analysis, class prediction is a crucial task. The goal of class prediction is to classify and predict the diagnostic category of a sample by its gene expression profile. This project studies class prediction by gene expression profiles and contains four main objectives. First, a new statistical method is proposed to select genes. Second, stochastic discrimination (SD) is proposed to build class predictors. The principle of SD is that it first generates many "weak" rules, each of which can serve as a classification method usually with a "big" classification error, and then combines these "weak rules" in a way to form a strong classifier having a low classification error rate. Multi-class SD predictors will be established by using the two-class technique. Theoretical results on the prediction accuracies of SD predictors will be investigated. Geometric properties of SD predictors will be explored. It is believed that these results will not only significantly enhance understanding the predictors but also constitute the important theoretical support for the application of predictors. Third, a novel procedure is proposed to compare various class predictors. It will address whether or not the true accuracies of predictors differ significantly by statistics, and if such a difference is significant, what range is this difference. Forth, an extensive evaluation of the proposed research work is proposed to be conducted through simulation and by applying the predictors to real life gene expression data sets and comparing the performance of SD predictors with that from other competing algorithms