Iterative bicluster-based least square framework for estimation of missing values in microarray gene expression data
نویسندگان
چکیده
منابع مشابه
Iterative bicluster-based least square framework for estimation of missing values in microarray gene expression data
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 prop...
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MOTIVATION Gene expression data often contain missing expression values. Effective missing value estimation methods are needed since many algorithms for gene expression data analysis require a complete matrix of gene array values. In this paper, imputation methods based on the least squares formulation are proposed to estimate missing values in the gene expression data, which exploit local simi...
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Microarray experiments generate data sets with information on the expression levels of thousands of genes in a set of biological samples. Unfortunately, such experiments often produce multiple missing expression values, normally due to various experimental problems. As many algorithms for gene expression analysis require a complete data matrix as input, the missing values have to be estimated i...
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Missing values are often encountered in gene expression data sets. Several imputation methods have been proposed to estimate these missing values. In this paper, a new impute approach based on bicluster is introduced. By using the method of minimization the coherence of subset of genes expression matrix, the missing value is estimated with a more accurate result to improve the overall coherence...
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ژورنال
عنوان ژورنال: Pattern Recognition
سال: 2012
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2011.10.012