Iterative bicluster-based least square framework for estimation of missing values in microarray gene expression data

PATTERN RECOGNITION

卷:45

Article

Cheng, K. O.¹  Law, N. F.¹  Siu, W. C.^1,2 

[1] Hong Kong Polytech Univ, Dept Elect & Informat Engn, Ctr Signal Proc, Hong Kong, Hong Kong, Peoples R China

[2] Hong Kong Polytech Univ, Dept Elect & Informat Engn EIE, Hong Kong, Hong Kong, Peoples R China

关键词: Missing value imputation;    Biclustering;    Iterative estimation;    Gene expression analysis;   

DOI  :  10.1016/j.patcog.2011.10.012

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

Free