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首页> 外文期刊>Mathematical Problems in Engineering >Missing Value Estimation for Microarray Data by Bayesian Principal Component Analysis and Iterative Local Least Squares
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Missing Value Estimation for Microarray Data by Bayesian Principal Component Analysis and Iterative Local Least Squares

机译：贝叶斯主成分分析和迭代局部最小二乘估计微阵列数据的缺失值

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摘要

Missing values are prevalent in microarray data, they course negative influence on downstream microarray analyses, and thus they should be estimated from known values. We propose a BPCA-iLLS method, which is an integration of two commonly used missing value estimation methods-Bayesian principal component analysis (BPCA) and local least squares (LLS). The inferior row-average procedure in LLS is replaced with BPCA, and the least squares method is put into an iterative framework. Comparative result shows that the proposed method has obtained the highest estimation accuracy across all missing rates on different types of testing datasets.
机译：缺失值在微阵列数据中很普遍，它们对下游微阵列分析产生负面影响，因此应从已知值进行估算。我们提出了BPCA-iLLS方法，该方法是两种常用的缺失值估计方法-贝叶斯主成分分析（BPCA）和局部最小二乘（LLS）的集成。用BPCA代替了LLS中较低的行平均过程，并将最小二乘方法放入了迭代框架中。比较结果表明，该方法在不同类型的测试数据集的所有遗漏率上均获得了最高的估计精度。

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  • 来源
    《Mathematical Problems in Engineering》 |2013年第3期|162938.1-162938.5|共5页
  • 作者

    Fuxi Shi; Dan Zhang; Jun Chen; Hamid Reza Karimi;

  • 作者单位

    College of Mechanical and Electronic Engineering, Northwest A&F University, No. 22 Xinong Road, Yangling, Xi'an, Shaanxi 712100, China;

    School of Electronics and Information Engineering, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an, Shaanxi 710049, China;

    College of Mechanical and Electronic Engineering, Northwest A&F University, No. 22 Xinong Road, Yangling, Xi'an, Shaanxi 712100, China;

    Department of Engineering, Faculty of Technology and Science, University of Agder, Service Box 509, 4898 Grimstad, Norway;

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