Missing-value estimation using linear and non-linear regression with Bayesian gene selection
Missing-value estimation using linear and non-linear regression with Bayesian gene selection
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DOI：
10.1093/bioinformatics/btg323
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发表时间：
2003-11-22
期刊：
影响因子：
5.8
通讯作者：
Dougherty, ER
中科院分区：
文献类型：
--
作者：
Zhou, XB;Wang, XD;Dougherty, ER
Motivation: Data from microarray experiments are usually in the form of large matrices of expression levels of genes under different experimental conditions. Owing to various reasons, there are frequently missing values. Estimating these missing values is important because they affect downstream analysis, such as clustering, classification and network design. Several methods of missing-value estimation are in use. The problem has two parts: (1) selection of genes for estimation and (2) design of an estimation rule.Results: We propose Bayesian variable selection to obtain genes to be used for estimation, and employ both linear and nonlinear regression for the estimation rule itself. Fast implementation issues for these methods are discussed, including the use of OR decomposition for parameter estimation. The proposed methods are tested on data sets arising from hereditary breast cancer and small round blue-cell tumors. The results compare very favorably with currently used methods based on the normalized root-mean-square error.
