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Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/4774

Title: Iterative bicluster-based least square framework for estimation of missing values in microarray gene expression data
Authors: Cheng, Kin-on
Law, Ngai-fong Bonnie
Siu, Wan-chi
Subjects: Missing value imputation
Iterative estimation
Gene expression analysis
Issue Date: Apr-2012
Publisher: Elsevier
Citation: Pattern recognition, Apr. 2012, v. 45, no. 4, p. 1281-1289.
Abstract: DNA microarray experiment inevitably generates gene expression data with missing values. An important and necessary pre-processing step is thus to impute these missing values. Existing imputation methods exploit gene correlation among all experimental conditions for estimating the missing values. However, related genes coexpress in subsets of experimental conditions only. In this paper, we propose to use biclusters, which contain similar genes under subset of conditions for characterizing the gene similarity and then estimating the missing values. To further improve the accuracy in missing value estimation, an iterative framework is developed with a stopping criterion on minimizing uncertainty. Extensive experiments have been conducted on artificial datasets, real microarray datasets as well as one non-microarray dataset. Our proposed biclusters-based approach is able to reduce errors in missing value estimation.
Description: DOI: 10.1016/j.patcog.2011.10.012
Rights: Pattern Recognition ©2011 Elsevier Ltd. The journal web site is located at http://www.sciencedirect.com.
Type: Journal/Magazine Article
URI: http://hdl.handle.net/10397/4774
ISSN: 0031-3203
Appears in Collections:EIE Journal/Magazine Articles

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