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

Cheng, Kin-On; Law, Ngai-Fong; Siu, Wan-Chi

doi:10.1016/j.patcog.2011.10.012

Cited by 47 publications

(23 citation statements)

References 25 publications

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“…An estimation of the quality of a bi cluster based on the non-linear correlation among genes and conditions simultaneously is performed in [9]. An iterative structure with a stopping criterion to minimize uncertainty and improve the accuracy is implemented in [8]. A technique to find out local patterns in large dataset using Non-smooth Non-negative Matrix Factorization is proposed in [12].…”

Section: Rel Ated Workmentioning

confidence: 99%

Bi-clustering of gene expression microarray using coarse grained Parallel Genetic Algorithm(CgPGA) with migration

Laishram¹,

Vipsita²

2015

2015 Annual IEEE India Conference (INDICON)

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Hi-clustering of gene expression micro array data deals with creating a sub-matrix that shows a high similarity across both genes and conditions. Hi-clustering aims at identifying several bi-clusters that reveal potential local patterns from a microarray matrix. In this paper, evolutionary algorithm is used to find bi-clusters of large size which have mean squared residue less than a given threshold, 15. Attention is also given to find bi-clusters with minimum overlapping among themselves by assigning weights to the elements of microarray matrix. Initially, Genetic Algorithm (GA) is implemented to derive bi-clusters from microarray matrix. From numerical simulations, it is observed that GA took too much time to converge so as to meet the stopping criteria. To further improve the performance of GA, Parallel GA (PGA) is implemented with an objective, so as to efficiently handle the problem of slow convergence encountered in traditional GA. A framework of Coarse grained Parallel Genetic Algorithm (CgPGA) for bi-clustering is implemented in this paper. The results obtained from CgPGA are quite encouraging as CgPGA took very less time to meet the stopping criteria. The bi-clusters derived by CgPGA are larger in size, which is one of the primary objective of bi-clustering problem. The experiment was performed on micro array dataset i.e. yeast Saccharomyces cerevisiae cell cycle.

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Section: Rel Ated Workmentioning

confidence: 99%

Bi-clustering of gene expression microarray using coarse grained Parallel Genetic Algorithm(CgPGA) with migration

Laishram¹,

Vipsita²

2015

2015 Annual IEEE India Conference (INDICON)

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“…K.O. Cheng et al proposed an iterative bicluster-based least squares imputation method (bicluster-iLLS) in 2012 [6], which utilizes a matrix R=B T A to select the most "correlated" genes (rows) and experiment conditions (columns) for every individual missing value in the target gene. Generally, iLLS achieves lower NRMSE compared with LLS, and an even lower result can be seen for bicluster-iLLS.…”

Section: The Lls Frameworkmentioning

confidence: 99%

“…Following the article of K.O. Cheng et al [6], we found the bicluster for a missing value j α in the form below:…”

Section: L 1 Approachmentioning

confidence: 99%

“…Several experiments on microarray missing value imputation by using sparse representation were implemented. We compared the results with those of traditional l 2 -norm approaches, i.e., the local least squares (LLS) [4], iterated local least squares (iLLS) [5], and iterative biclusterbased least squares (bicluster-iLLS) [6]. The results indicated that the l 1 -norm minimization does not show its advantages in microarray datasets.…”

Section: Introductionmentioning

confidence: 99%

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Performance evaluation of L<inf>1</inf>-norm-based microarray missing value imputation

Meng

Cai

2012

2012 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC 2012)

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l 1 -norm minimization was utilized in the imputation of microarray missing values, which is an important procedure in bioinformatics experiments. Two l 1 approaches, based on the framework of local least squares (LLS) and iterative biclusterbased least squares (bicluster-iLLS) respectively, were employed. Imputed datasets of the l 1 approaches were compared with those of traditional l 2 methods. The imputation error rates showed that the assumption of sparsity is not supported by the microarray datasets. Singular value decompositions in biclusters and in the neighborhoods of target gens were computed to show the structure of a microarray dataset. The coefficients of l 1 minimization solutions were also analyzed to reveal possible reasons for the performance of l 1 approaches.

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“…LLS uses a multiple regression model to impute the missing values from K nearest neighbor (KNN) genes of the target gene. Due to the simplicity and effectiveness of LLS, various LLS-derived methods have been proposed, including iterated local least squares (iLLS) [11], sequential local least squares [12], weighted local least squares [13], and iterative bicluster-based least squares (bi-iLS) [14]. Other famous local approaches include KNN [8], least squares (LS) [15], Gaussian mixture clustering [16], and a recently proposed autoregressive model-based least-squares (ARLS) method [17].…”

Section: Introductionmentioning

confidence: 99%

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

Meng

Cai

Yan

2014

IEEE J. Biomed. Health Inform.

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Data generated from microarray experiments often suffer from missing values. As most downstream analyses need full matrices as input, these missing values have to be estimated. Bayesian principal component analysis (BPCA) is a well-known microarray missing value estimation method, but its performance is not satisfactory on datasets with strong local similarity structure. A bicluster-based BPCA (bi-BPCA) method is proposed in this paper to fully exploit local structure of the matrix. In a bicluster, the most correlated genes and experimental conditions with the missing entry are identified, and BPCA is conducted on these biclusters to estimate the missing values. An automatic parameter learning scheme is also developed to obtain optimal parameters. Experimental results on four real microarray matrices indicate that bi-BPCA obtains the lowest normalized root-mean-square error on 82.14% of all missing rates.

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

Cited by 47 publications

References 25 publications

Bi-clustering of gene expression microarray using coarse grained Parallel Genetic Algorithm(CgPGA) with migration

Bi-clustering of gene expression microarray using coarse grained Parallel Genetic Algorithm(CgPGA) with migration

Performance evaluation of L<inf>1</inf>-norm-based microarray missing value imputation

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

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