:: DESCRIPTION
Bicluster is a seed-based algorithm that identifies coherent genes in an exhaustive, but efficient manner.
::DEVELOPER
AUSTRALIAN PROSTATE CANCER RESEARCH CENTRE.
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
An J, Liew AW-C, Nelson CC (2012)
Seed-Based Biclustering of Gene Expression Data.
PLoS ONE 7(8): e42431. doi:10.1371/journal.pone.0042431
:: DESCRIPTION
Furby is an interactive visualization technique for analyzing biclustering results.
::DEVELOPER
:: SCREENSHOTS
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
BMC Bioinformatics. 2014;15 Suppl 6:S4. doi: 10.1186/1471-2105-15-S6-S4. Epub 2014 May 16.
Furby: fuzzy force-directed bicluster visualization.
Streit M, Gratzl S, Gillhofer M, Mayr A, Mitterecker A, Hochreiter S.
:: DESCRIPTION
Bi-Force is a novel way of modeling the problem as combinatorial optimization problem on graphs: Weighted Bi-Cluster Editing. It is a very flexible model that can handle arbitrary kinds of multi-condition data sets (not limited to gene expression).
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Bi-Force: large-scale bicluster editing and its application to gene expression data biclustering.
Sun P, Speicher NK, Röttger R, Guo J, Baumbach J.
Nucleic Acids Res. 2014 May;42(9):e78. doi: 10.1093/nar/gku201.
:: DESCRIPTION
FABIA (Factor Analysis for Bicluster Acquisition) is a model-based technique for biclustering, that is clustering rows and columns simultaneously. FABIA is a multiplicative model that assumes realistic non-Gaussian signal distributions with heavy tails. FABIA utilizes well understood model selection techniques like variational approaches and applies the Bayesian framework. The generative framework allows FABIA to determine the information content of each bicluster to separate spurious biclusters from true biclusters. On 100 simulated data sets with known true, artificially implanted biclusters, FABIA clearly outperformed all 11 competitors. FABIA was tested on microarray data sets which known, biological verfified subclusters and performed on average best out of 11 biclustering approaches.
::DEVELOPER
Institute of Bioinformatics, Johannes Kepler University Linz
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Sepp Hochreiter, Ulrich Bodenhofer, Martin Heusel, Andreas Mayr, Andreas Mitterecker, Adetayo Kasim, Tatsiana Khamiakova, Suzy Van Sanden, Dan Lin, Willem Talloen, Luc Bijnens, Hinrich W.H. Göhlmann, Ziv Shkedy, and Djork-Arné Clevert.
FABIA: Factor Analysis for Bicluster Acquisition,
Bioinformatics 2010, 26(12):1520-1527,
:: DESCRIPTION
LEB is a software of biclustering gene expression data, based on Localize-and-Extract Biclusters
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
“Biclustering Expression Data Based on Expanding Localized Substructures” ,
C. Erten, M. Sozdinler,
Proc. of Int. Conf. on Bioinformatics and Computational Biology (BICOB 09).
:: DESCRIPTION
EDISA (Extended Dimension Iterative Signature Algorithm) is a novel probabilistic clustering approach for 3D gene-condition-time datasets. Based on mathematical definitions of gene expression modules, the EDISA samples initial modules from the dataset which are then refined by removing genes and conditions until they comply with the module definition. A subsequent extension step ensures gene and condition maximality. We applied the algorithm to a synthetic dataset and were able to successfully recover the implanted modules over a range of background noise intensities.
::DEVELOPER
the Center for Bioinformatics Tübingen (Zentrum für Bioinformatik Tübingen, ZBIT).
:: SCREENSHOTS
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Jochen Supper, Martin Strauch, Dierk Wanke, Klaus Harter, Andreas Zell:
EDISA: extracting biclusters from multiple time-series of gene expression profiles
BMC Bioinformatics 2007, 8:334
:: DESCRIPTION
BiVisu is an open-source software tool for detecting and visualizing biclusters embedded in a gene expression matrix.
::DEVELOPER
:: SCREENSHOTS
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
K.O. Cheng, N.F. Law, W.C. Siu and T.H. Lau,
“ BiVisu: Software Tool for Bicluster Detection and Visualization “,
Bioinformatics, Vol. 23, No. 17, 2342-2344, 2007
:: DESCRIPTION
relax_bicluster is a biclustering algorithm based the probabilistic relaxation labeling framework for discovering geometric biclusters of gene expression data.
::DEVELOPER
Hong Yan , Signal Processing Lab at City University of Hong Kong
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
Hongya Zhao et al.
A probabilistic relaxation labeling framework for reducing the noise effect in geometric biclustering of gene expression data
Journal Pattern Recognition Volume 42 Issue 11, November, 2009 Pages 2578-2588
:: DESCRIPTION
ExpressionView is an R package that provides an interactive environment to explore biclusters identified in gene expression data. A sophisticated ordering algorithm is used to present the biclusters in a visually appealing layout. From this overview, the user can select individual biclusters and access all the biologically relevant data associated with it. The package is aimed to facilitate the collaboration between bioinformaticians and life scientists who are not familiar with the R language.
::DEVELOPER
Computational Biology Group ,Department of Medical Genetics, University of Lausanne
:: SCREENSHOTS
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
ExpressionView–an interactive viewer for modules identified in gene expression data.
Lüscher A, Csárdi G, de Lachapelle AM, Kutalik Z, Peter B, Bergmann S.
Bioinformatics. 2010 Aug 15;26(16):2062-3. Epub 2010 Jul 29.
