A Bicluster-Based Bayesian Principal Component Analysis Method for Microarray Missing Value Estimation

Meng, Fanchi; Cai, Chang; Yan, Hong

doi:10.1109/jbhi.2013.2284795

Cited by 23 publications

(10 citation statements)

References 28 publications

(60 reference statements)

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“…Suguna and Thanushkodi [13] combined genetic algorithm (GA) and KNN to improve classification performance of the hybrid algorithm. On the basis of KNN, Meng et al [14] sorted missing rates of data in an ascending order to complete the filling work and expanded the filled data into the adjacent selection set.Žukovič and Hristopulos [15] put forward a Directional Gradient-Curvature method based on an objective function. Zhang et al [16] indicated that grey relation degree was more suitable than Euclidean distance or other distances to calculate similarities between two samples.…”

Section: Solving the Problem Of Missing Partial Datamentioning

confidence: 99%

Demand Prediction of Emergency Supplies under Fuzzy and Missing Partial Data

Zhang

Qiu

et al. 2019

Discrete Dynamics in Nature and Society

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An accurate demand prediction of emergency supplies according to disaster information and historical data is an important research subject in emergency rescue. This study aims at improving supplies demand prediction accuracy under partial data fuzziness and missing. The main contributions of this study are summarized as follows. (1) In view that it is difficult for the turning point of the whitenization weight function to determine fuzzy data, two computational formulas solving “core” of fuzzy interval grey numbers were proposed, and the obtained “core” replaced primary fuzzy information so as to reach the goal of transforming uncertain information into certain information. (2) For partial data missing, the improved grey k-nearest neighbor (GKNN) algorithm was put forward based on grey relation degree and K-nearest neighbor (KNN) algorithm. Weights were introduced in the filling and logic test conditions were added after filling so that filling results were of higher truthfulness and accuracy. (3) The preprocessed data are input into the improved algorithm based on the genetic algorithm and BP neural networks (GABP) to obtain the demand prediction model. Finally the calculation presents that the prediction accuracy and its stability are improved at the five-group comparative tests of calculated examples of actual disasters. The experiments indicated that the supplies demand prediction model under data fuzziness and missing proposed in this study was of higher prediction accuracy.

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Section: Solving the Problem Of Missing Partial Datamentioning

confidence: 99%

Demand Prediction of Emergency Supplies under Fuzzy and Missing Partial Data

Zhang

Qiu

et al. 2019

Discrete Dynamics in Nature and Society

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“…The second dataset contains 5766 complete genes with 14 experiments, named Elu. The third dataset, Ronen, includes two time series in yeast from a study of response to environmental changes ( http://ncbi.nlm.nih.gov/Projects/geo/query/acc.cgi?acc=GSE4158 ) [ 30 ], and is also used in [ 11 ] to assess bi-BPCA. The matrix contains 10749 genes in 26 experiments originally.…”

Section: Recursive Mutual Imputationmentioning

confidence: 99%

“…Algorithms on imputing missing values can be classified into four categories [ 11 , 12 ]: global approach, local approach, hybrid approach and knowledge assisted approach. Each of them has its own characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…Correspondingly, local approach uses the potential local information to estimate the missing values, such as Local Least Squares (LLS) and Weighted K-Nearest Neighbor imputation (WKNN) [ 14 ]. LLS estimates missing values using a multiple regression model [ 11 ] based on K-nearest neighboring genes with respect to the target gene. Currently, several LLS variants have been proposed to improve algorithm performance in different aspects, such as Iterated Local Least Squares imputation (ItrLLS) [ 15 ], sequential Local Least Squares (sLLS) [ 16 ], weighted Local Least Squares (wLLS) [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, many novel imputation algorithms have been developed, such as bicluster-based impute (BIC) [ 22 ], bicluster-based Least Square (bi-iLS) [ 23 ] and bicluster-based Bayesian Principal Component Analysis (bi-BPCA) [ 11 ]. Although they properly utilize data local structure and global correlation information inspired by bicluster algorithm, and a novel framework for missing value estimation is also designed, they still just use them separately without considering the advantage of their combination.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid imputation approach for microarray missing value estimation

Zhao

et al. 2015

BMC Genomics

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BackgroundMissing data is an inevitable phenomenon in gene expression microarray experiments due to instrument failure or human error. It has a negative impact on performance of downstream analysis. Technically, most existing approaches suffer from this prevalent problem. Imputation is one of the frequently used methods for processing missing data. Actually many developments have been achieved in the research on estimating missing values. The challenging task is how to improve imputation accuracy for data with a large missing rate.MethodsIn this paper, induced by the thought of collaborative training, we propose a novel hybrid imputation method, called Recursive Mutual Imputation (RMI). Specifically, RMI exploits global correlation information and local structure in the data, captured by two popular methods, Bayesian Principal Component Analysis (BPCA) and Local Least Squares (LLS), respectively. Mutual strategy is implemented by sharing the estimated data sequences at each recursive process. Meanwhile, we consider the imputation sequence based on the number of missing entries in the target gene. Furthermore, a weight based integrated method is utilized in the final assembling step.ResultsWe evaluate RMI with three state-of-art algorithms (BPCA, LLS, Iterated Local Least Squares imputation (ItrLLS)) on four publicly available microarray datasets. Experimental results clearly demonstrate that RMI significantly outperforms comparative methods in terms of Normalized Root Mean Square Error (NRMSE), especially for datasets with large missing rates and less complete genes.ConclusionsIt is noted that our proposed hybrid imputation approach incorporates both global and local information of microarray genes, which achieves lower NRMSE values against to any single approach only. Besides, this study highlights the need for considering the imputing sequence of missing entries for imputation methods.

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