Literature DB >> 16551664

BicAT: a biclustering analysis toolbox.

Simon Barkow1, Stefan Bleuler, Amela Prelic, Philip Zimmermann, Eckart Zitzler.   

Abstract

SUMMARY: Besides classical clustering methods such as hierarchical clustering, in recent years biclustering has become a popular approach to analyze biological data sets, e.g. gene expression data. The Biclustering Analysis Toolbox (BicAT) is a software platform for clustering-based data analysis that integrates various biclustering and clustering techniques in terms of a common graphical user interface. Furthermore, BicAT provides different facilities for data preparation, inspection and postprocessing such as discretization, filtering of biclusters according to specific criteria or gene pair analysis for constructing gene interconnection graphs. The possibility to use different biclustering algorithms inside a single graphical tool allows the user to compare clustering results and choose the algorithm that best fits a specific biological scenario. The toolbox is described in the context of gene expression analysis, but is also applicable to other types of data, e.g. data from proteomics or synthetic lethal experiments. AVAILABILITY: The BicAT toolbox is freely available at http://www.tik.ee.ethz.ch/sop/bicat and runs on all operating systems. The Java source code of the program and a developer's guide is provided on the website as well. Therefore, users may modify the program and add further algorithms or extensions.

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Year:  2006        PMID: 16551664     DOI: 10.1093/bioinformatics/btl099

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  55 in total

1.  Human behavioral informatics in genetic studies of neuropsychiatric disease: multivariate profile-based analysis.

Authors:  Cinnamon S Bloss; Kelly M Schiabor; Nicholas J Schork
Journal:  Brain Res Bull       Date:  2010-04-28       Impact factor: 4.077

2.  Biclustering of adverse drug events in the FDA's spontaneous reporting system.

Authors:  R Harpaz; H Perez; H S Chase; R Rabadan; G Hripcsak; C Friedman
Journal:  Clin Pharmacol Ther       Date:  2010-12-29       Impact factor: 6.875

3.  Bi-Force: large-scale bicluster editing and its application to gene expression data biclustering.

Authors:  Peng Sun; Nora K Speicher; Richard Röttger; Jiong Guo; Jan Baumbach
Journal:  Nucleic Acids Res       Date:  2014-03-20       Impact factor: 16.971

4.  A novel non-overlapping bi-clustering algorithm for network generation using living cell array data.

Authors:  E Yang; P T Foteinou; K R King; M L Yarmush; I P Androulakis
Journal:  Bioinformatics       Date:  2007-09-07       Impact factor: 6.937

5.  An efficient voting algorithm for finding additive biclusters with random background.

Authors:  Jing Xiao; Lusheng Wang; Xiaowen Liu; Tao Jiang
Journal:  J Comput Biol       Date:  2008-12       Impact factor: 1.479

6.  Construction of gene regulatory networks using biclustering and Bayesian networks.

Authors:  Fadhl M Alakwaa; Nahed H Solouma; Yasser M Kadah
Journal:  Theor Biol Med Model       Date:  2011-10-22       Impact factor: 2.432

7.  Biclustering as a method for RNA local multiple sequence alignment.

Authors:  Shu Wang; Robin R Gutell; Daniel P Miranker
Journal:  Bioinformatics       Date:  2007-10-06       Impact factor: 6.937

8.  A semi-parametric Bayesian model for unsupervised differential co-expression analysis.

Authors:  Johannes M Freudenberg; Siva Sivaganesan; Michael Wagner; Mario Medvedovic
Journal:  BMC Bioinformatics       Date:  2010-05-07       Impact factor: 3.169

9.  An effective tri-clustering algorithm combining expression data with gene regulation information.

Authors:  Ao Li; David Tuck
Journal:  Gene Regul Syst Bio       Date:  2009-04-15

10.  BiGGEsTS: integrated environment for biclustering analysis of time series gene expression data.

Authors:  Joana P Gonçalves; Sara C Madeira; Arlindo L Oliveira
Journal:  BMC Res Notes       Date:  2009-07-07
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