Journal
PATTERN RECOGNITION
Volume 45, Issue 4, Pages 1281-1289Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2011.10.012
Keywords
Missing value imputation; Biclustering; Iterative estimation; Gene expression analysis
Funding
- Center for Signal Processing, the department of Electronic and Information Engineering, the Hong Kong Polytechnic University [A-PJ24, G-U876]
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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. (C) 2011 Elsevier Ltd. All rights reserved.
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