BiVisu: software tool for bicluster detection and visualization

Cheng, Kin-On; Law, Ngai-Fong; Siu, Wan-Chi; Lau, T. H.

doi:10.1093/bioinformatics/btm338

Cited by 66 publications

(49 citation statements)

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“…Often, the gene similarity is measured based on the similarity of the expression profiles across all experimental conditions [15]. In reality, genes are co-expressed under certain conditions only [16][17][18][19][20]. Hence the gene similarity should be measured by considering only those related experimental conditions, rather than all the conditions.…”

Section: Introductionmentioning

confidence: 99%

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

Cheng

Law

Siu

2012

Pattern Recognition

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DNA microarray experiment inevitably generates gene expression data with missing values. An important and necessary pre-processing step is thus to impute these missing values. Existing imputation methods exploit gene correlation among all experimental conditions for estimating the missing values. However, related genes coexpress in subsets of experimental conditions only. In this paper, we propose to use biclusters which contain similar genes under subset of conditions for characterizing the gene similarity and then estimating the missing values. To further improve the accuracy in missing value estimation, an iterative framework is developed with a stopping criterion on minimizing uncertainty. Extensive experiments have been conducted on artificial datasets, real microarray datasets as well as one non-microarray dataset. Our proposed biclusters-based approach This is the Pre-Published Version.2 is able to reduce errors in missing value estimation.

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Section: Introductionmentioning

confidence: 99%

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

Cheng

Law

Siu

2012

Pattern Recognition

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“…This perspective has been implemented in multiple software tools among which the most popular are BiVoc [8], BiVisu [9], BicAT [10] and its extension BicATPlus [11], BicOverlapper [12], [13], Furby [14], Bicluster Viewer [15] or BiGGEsTS [16]. A single bicluster is usually resorted and drawn in the upper left corner with its rows and columns rearranged.…”

Section: A Methods Of Visualization Of Biclustersmentioning

confidence: 99%

QtBiVis: a software toolbox for visual analysis of biclustering experiment

Pańszczyk

Orzechowski

2016

Annals of Computer Science and Information Systems

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Abstract-In this article we introduce QtBiVis -a novel software intended for the comparative analysis of biclustering results. This modular tool has been efficiently implemented in C++ with Qt framework GUI. It may be successfully used for coverage analysis of the results of biclustering as well filtering or sorting biclusters by Gene Ontology (GO) identifiers or bicluster enrichment values. It may also be useful for parameter studies of biclustering algorithms. In future releases we plan to add different modules for visualizing and comparing different GO terms and biclusters.

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“…-The first statistic we will consider has two components s (1) and s (2) : resp. the function computing the sum of the set of values it is evaluated on, and the function computing the sum of their squares:…”

Section: Notation and Basic Conceptsmentioning

confidence: 99%

“…3, the information on the empirical mean and variance of a set of values is equivalent with information on their sum and sum of squares, computed by the functions s (1) and s (2) respectively. Thus, to consider background knowledge on the empirical mean and variance for a set of elements E, we need the following two functions as constraint functions in Eq.…”

Section: Encoding Means and Variances As Prior Knowledgementioning

confidence: 99%

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Maximum Entropy Models for Iteratively Identifying Subjectively Interesting Structure in Real-Valued Data

Kontonasios

Vreeken

Bie

2013

Advanced Information Systems Engineering

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Abstract. In exploratory data mining it is important to assess the significance of results. Given that analysts have only limited time, it is important that we can measure this with regard to what we already know. That is, we want to be able to measure whether a result is interesting from a subjective point of view. With this as our goal, we formalise how to probabilistically model real-valued data by the Maximum Entropy principle, where we allow statistics on arbitrary sets of cells as background knowledge. As statistics, we consider means and variances, as well as histograms. The resulting models allow us to assess the likelihood of values, and can be used to verify the significance of (possibly overlapping) structures discovered in the data. As we can feed those structures back in, our model enables iterative identification of subjectively interesting structures. To show the flexibility of our model, we propose a subjective informativeness measure for tiles, i.e. rectangular sub-matrices, in real-valued data. The Information Ratio quantifies how strongly the knowledge of a structure reduces our uncertainty about the data with the amount of effort it would cost to consider it. Empirical evaluation shows that iterative scoring effectively reduces redundancy in ranking candidate tiles-showing the applicability of our model for a range of data mining fields aimed at discovering structure in real-valued data.

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BiVisu: software tool for bicluster detection and visualization

Abstract: BiVisu has been developed in Matlab and is available at http://www.eie.polyu.edu.hk/~nflaw/Biclustering/.

Cited by 66 publications

References 3 publications

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

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

QtBiVis: a software toolbox for visual analysis of biclustering experiment

Maximum Entropy Models for Iteratively Identifying Subjectively Interesting Structure in Real-Valued Data

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