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Missing value imputation for gene expression data: computational techniques to recover missing data from available information
"... Abstract 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 comprehen ..."
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Abstract 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 techniques and how they utilize local or global information from within the data, or their use of domain knowledge during imputation. In addition, we describe how the imputation results can be validated and the different ways to assess the performance of different imputation algorithms, as well as a discussion on some possible future research directions. It is hoped that this review will give the readers a good understanding of the current development in this field and inspire them to come up with the next generation of imputation algorithms.
BMC Bioinformatics BioMed Central Methodology article
"... Discovering biclusters in gene expression data based on high-dimensional linear geometries ..."
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Discovering biclusters in gene expression data based on high-dimensional linear geometries
Biclustering of Microarray Data based on Modular Singular Value Decomposition
"... Keywords: Gene expression data, Microarray data, SVD, Biclustering. Abstract. Unsupervised machine learning methods are widely used in the analysis of gene expression data obtained from microarray experiments. Clustering of data is one of the most popular approaches of analyzing gene expression data ..."
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Keywords: Gene expression data, Microarray data, SVD, Biclustering. Abstract. Unsupervised machine learning methods are widely used in the analysis of gene expression data obtained from microarray experiments. Clustering of data is one of the most popular approaches of analyzing gene expression data. Recently, biclustering approach which has shown to be remarkably effective in a variety of applications that perform simultaneous clustering on the row and column dimension of the data matrix. In this paper, we present a new approach to biclustering called the Modular Singular Value Decomposition (M-SVD-BC) for gene expression. Experimental study on standard datasets demonstrated the effectiveness of the algorithm in gene expression data. 1
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"... Identification of coherent patterns in gene expression data using an efficient biclustering algorithm and parallel coordinate visualization ..."
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Identification of coherent patterns in gene expression data using an efficient biclustering algorithm and parallel coordinate visualization
GENETIC ALGORITHM BASED DETECTION OF GENERAL LINEAR BICLUSTERS
"... Clustering methods classify patterns into clusters using the entire set of attributes of patterns in the similarity measurement. In plenty of cases, patterns are similar under a subset of attributes only. The class of methods that cluster patterns based on subsets of attributes is called biclusterin ..."
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Clustering methods classify patterns into clusters using the entire set of attributes of patterns in the similarity measurement. In plenty of cases, patterns are similar under a subset of attributes only. The class of methods that cluster patterns based on subsets of attributes is called biclustering. Biclustering simultaneously groups on both rows and columns of a data matrix and has been applied to various fields, especially gene expression data. However, the biclustering problem is inherently intractable and computationally complex. In recent years, several biclustering algorithms which are based on linear coherent model have been proposed. In this paper, we introduce a novel GA-based algorithm that uses hyperplane to describe the linear relationships between rows (genes) in a sub-matrix (bicluster). The performance of our algorithm is tested via simulated data, gene expression data and compared with several other bicluster methods. Keywords: Biclustering; Linear coherent patterns; Shifting and scaling
Biclustering Analysis for Pattern Discovery: Current Techniques, Comparative Studies and Applications
"... Biclustering analysis is a useful methodology to discover the local coherent patterns hidden in a data matrix. Unlike the traditional clustering procedure, which searches for groups of coherent patterns using the entire feature set, biclustering performs simultaneous pattern classification in both r ..."
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Biclustering analysis is a useful methodology to discover the local coherent patterns hidden in a data matrix. Unlike the traditional clustering procedure, which searches for groups of coherent patterns using the entire feature set, biclustering performs simultaneous pattern classification in both row and column directions in a data matrix. The technique has found useful applications in many fields but notably in bioinformatics. In this paper, we give an overview of the biclustering problem and review some existing biclustering algorithms in terms of their underlying methodology, search strategy, detected bicluster patterns, and validation strategies. Moreover, we show that geometry of biclustering patterns can be used to solve biclustering problems effectively. Well-known methods in signal and image analysis, such as the Hough transform and relaxation labeling, can be employed to detect the geometrical biclustering patterns. We present performance evaluation results for several of the well known biclustering algorithms, on both artificial and real gene expression datasets. Finally, several interesting applications of biclustering are discussed.
