The Implementation and Application of the Microarray Preprocessing Generalized PDNN Model

Wei Wei, Lin Wan, M. Qian, Minghua Deng
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引用次数: 1

Abstract

The preprocessing of the Microarray data is a hot topic in the bioinformatics research. The key point of a successful preprocessing method is to remove the noise of nonspecific binding and to keep the information of specific binding as much as possible. One way to solve these problems is to understand the principle of the binding between probes and target sequences, and to distinguish specific binding from nonspecific binding correctly. In this paper, we introduce MM probe intensities into position dependent nearest neighbor (PDNN) model, which contain much information of nonspecific binding.We use two-step model to estimate the parameters,which can simplify the computation. Based on the Wilcoxon rank test, we can determine whether a gene is present, with which we can obtain the training data set for the specific binding and non specific binding parameters. We also apply our model to gene expression data (HGU133plus2.0 and HGU133A) . We find that all these improvements increase the precision and stability, and show better result compared to the other four methods( Mas5.0, dChip, RMA and PDNN ).
微阵列预处理广义PDNN模型的实现与应用
微阵列数据预处理是生物信息学研究的热点。一种成功的预处理方法的关键是去除非特异性绑定的噪声,并尽可能地保留特异性绑定的信息。解决这些问题的途径之一是理解探针与靶序列的结合原理,正确区分特异性结合与非特异性结合。将MM探针强度引入到包含大量非特异性结合信息的位置依赖最近邻(PDNN)模型中。我们采用两步模型来估计参数,简化了计算。通过Wilcoxon秩检验,我们可以确定某个基因是否存在,从而获得特异性结合参数和非特异性结合参数的训练数据集。我们还将我们的模型应用于基因表达数据(HGU133plus2.0和HGU133A)。我们发现所有这些改进都提高了精度和稳定性,并且与其他四种方法(Mas5.0, dChip, RMA和PDNN)相比显示出更好的结果。
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