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149
Nearest neighbour approach in the least-squares imputation algorithms
- JOURNAL OF INFORMATION SCIENCES
, 2005
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Weighted Local Least Squares Imputation Method for Missing Value Estimation
"... Abstract Missing values often exist in the data of gene expression microarray experiments. A number of methods such as the Row Average (RA) method, KNNimpute algorithm and SVDimpute algorithm have been proposed to estimate the missing values. Recently, Kim et al. proposed a Local Least Squares Imput ..."
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Imputation (LLSI) method for estimating the missing values. In this paper, we propose a Weighted Local Least Square Imputation (WLLSI) method for missing values estimation. WLLSI allows training on the weighting and therefore can take advantage of both the LLSI method and the RA method. Numerical results
Missing value estimation for DNA microarray gene expression data: local least squares imputation
- BIOINFORMATICS
, 2005
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THE INTERNATIONAL SYMPOSIUM ON OPTIMIZATION AND SYSTEMS BIOLOGY (OSB 2007) 1 Weighted Local Least Squares Imputation Method for Missing Value Estimation
"... Missing values often exist in the data of gene expression microarray experiments. A number of methods such as the Row Average (RA) method, KNNimpute algorithm and SVDimpute algorithm have been proposed to estimate the missing values. Recently, Kim et al. proposed a Local Least Squares Imputation (LL ..."
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Missing values often exist in the data of gene expression microarray experiments. A number of methods such as the Row Average (RA) method, KNNimpute algorithm and SVDimpute algorithm have been proposed to estimate the missing values. Recently, Kim et al. proposed a Local Least Squares Imputation
Article Multiple Imputation of Squared Terms
"... We propose a new multiple imputation technique for imputing squares. Cur-rent methods yield either unbiased regression estimates or preserve data relations.Nomethod, however, seems to deliver both,which limits researchers in the implementation of regression analysis in the presence of missing data. ..."
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We propose a new multiple imputation technique for imputing squares. Cur-rent methods yield either unbiased regression estimates or preserve data relations.Nomethod, however, seems to deliver both,which limits researchers in the implementation of regression analysis in the presence of missing data
MICROARRAY MISSING VALUE IMPUTATION BY ITERATED LOCAL LEAST SQUARES ∗
"... Microarray gene expression data often contains missing values resulted from various reasons. However, most of the gene expression data analysis algorithms, such as clustering, classification and network design, require complete information, that is, without any missing values. It is therefore very i ..."
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important to accurately impute the missing values before applying the data analysis algorithms. In this paper, an Iterated Local Least Squares Imputation method (ILLsimpute) is proposed to estimate the missing values. In ILLsimpute, a similarity threshold is learned using known expression values
A New Data Imputing Algorithm
"... DNA microarray analysis has become the most widely used functional genomics approach in the bioinformatics field. Microarray gene expression data often contains missing values due to various reasons. Clustering gene expression data algorithms requires having complete information. This means that the ..."
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that there shouldn't be any missing values. In this paper, a clustering method is proposed, called "Clustering Local Least Square Imputation method (ClustLLsimpute)", to estimate the missing values. In ClustLLsimpute, a complete dataset is obtained by removing each row with missing values. K clusters
Imputation of missing values in DNA microarray gene expression data
- In Proceedings of the IEEE Computational Systems Bioinformatics Conference
, 2004
"... Most multivariate statistical methods for gene expression data require a complete matrix of gene array values. In this paper, a imputation method based on least squares formulation is proposed to estimate missing values. It exploits local similarity structures in the data as well as least squares op ..."
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Cited by 3 (0 self)
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Most multivariate statistical methods for gene expression data require a complete matrix of gene array values. In this paper, a imputation method based on least squares formulation is proposed to estimate missing values. It exploits local similarity structures in the data as well as least squares
BIOINFORMATICS ORIGINAL PAPER
"... doi:10.1093/bioinformatics/bth499 Missing value estimation for DNA microarray gene expression data: local least squares imputation ..."
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doi:10.1093/bioinformatics/bth499 Missing value estimation for DNA microarray gene expression data: local least squares imputation
Analysis of incomplete climate data: Estimation of mean values and covariance matrices and imputation of missing values
, 2001
"... Estimating the mean and the covariance matrix of an incomplete dataset and filling in missing values with imputed values is generally a nonlinear problem, which must be solved iteratively. The expectation maximization (EM) algorithm for Gaussian data, an iterative method both for the estimation of m ..."
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Cited by 109 (4 self)
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method in which a continuous regularization parameter controls the filtering of the noise in the data. The regularization parameter is determined by generalized cross-validation, such as to minimize, approximately, the expected mean squared error of the imputed values. The regularized EM algorithm can
Results 1 - 10
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149
