Use of biclustering for missing value imputation in gene expression data
نویسندگان
چکیده
منابع مشابه
Use of biclustering for missing value imputation in gene expression data
DNA microarray data always contains missing values. As subsequent analysis such as biclustering can only be applied on complete data, these missing values have to be imputed before any biclusters can be detected. Existing imputation methods exploit coherence among expression values in the microarray data. In view that biclustering attempts to find correlated expression values within the data, w...
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Microarray gene expression data generally suffers from missing value problem due to a variety of experimental reasons. Since the missing data points can adversely affect downstream analysis, many algorithms have been proposed to impute missing values. In this survey, we provide a comprehensive review of existing missing value imputation algorithms, focusing on their underlying algorithmic techn...
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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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Missing values has been a common problem in gene expression studies and have a significance effect on the interpretation of the final data. Many bioinformatics analysis tools especially for cancer classification and prediction require complete sets of data matrix. Therefore, development of missing value imputation algorithms is required to solve this particular problem. In this paper, we presen...
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ژورنال
عنوان ژورنال: Artificial Intelligence Research
سال: 2013
ISSN: 1927-6982,1927-6974
DOI: 10.5430/air.v2n2p96