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Missing value estimation for microarray data by Bayesian principal component analysis and iterative local least squares

Shi, Fuxi; Zhang, Dan; Chen, Jun; Karimi, Hamid Reza
Journal article, Peer reviewed
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Shi-2013-Missing Value Estimation for Microarr.pdf (1.472Mb)
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http://hdl.handle.net/11250/136971
Issue date
2013
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  • Scientific Publications in Engineering [277]
Original version
Shi, F.X., Zhang, D., Chen, J., & Karimi, H.R. (2013). Missing value estimation for microarray data by Bayesian principal component analysis and iterative local least squares. Mathematical Problems in Engineering. doi: 10.1155/2013/162938   10.1155/2013/162938
Abstract
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.
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Published version of an article from the journal: Mathematical Problems in Engineering. Also available from Hindawi: http://dx.doi.org/10.1155/2013/162938
Publisher
Hindawi
Journal
Mathematical Problems in Engineering

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