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      jClust: a clustering and visualization toolbox

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          Abstract

          jClust is a user-friendly application which provides access to a set of widely used clustering and clique finding algorithms. The toolbox allows a range of filtering procedures to be applied and is combined with an advanced implementation of the Medusa interactive visualization module. These implemented algorithms are k-Means, Affinity propagation, Bron–Kerbosch, MULIC, Restricted neighborhood search cluster algorithm, Markov clustering and Spectral clustering, while the supported filtering procedures are haircut, outside–inside, best neighbors and density control operations. The combination of a simple input file format, a set of clustering and filtering algorithms linked together with the visualization tool provides a powerful tool for data analysis and information extraction.

          Availability: http://jclust.embl.de/

          Contact: pavlopou@ 123456embl.de ; rschneid@ 123456embl.de ; skossida@ 123456bioacademy.gr

          Supplementary information: Supplementary data are available at Bioinformatics online.

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          Algorithm 457: finding all cliques of an undirected graph

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            Protein complex prediction via cost-based clustering.

            Understanding principles of cellular organization and function can be enhanced if we detect known and predict still undiscovered protein complexes within the cell's protein-protein interaction (PPI) network. Such predictions may be used as an inexpensive tool to direct biological experiments. The increasing amount of available PPI data necessitates an accurate and scalable approach to protein complex identification. We have developed the Restricted Neighborhood Search Clustering Algorithm (RNSC) to efficiently partition networks into clusters using a cost function. We applied this cost-based clustering algorithm to PPI networks of Saccharomyces cerevisiae, Drosophila melanogaster and Caenorhabditis elegans to identify and predict protein complexes. We have determined functional and graph-theoretic properties of true protein complexes from the MIPS database. Based on these properties, we defined filters to distinguish between identified network clusters and true protein complexes. Our application of the cost-based clustering algorithm provides an accurate and scalable method of detecting and predicting protein complexes within a PPI network.
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              Medusa: a simple tool for interaction graph analysis.

              Medusa is a Java application for visualizing and manipulating graphs of interaction, such as data from the STRING database. It features an intuitive user interface developed with the help of biologists. Medusa is optimized for accessing protein interaction data from STRING, but can be used for any type of graph from any scientific field.
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                Author and article information

                Journal
                Bioinformatics
                bioinformatics
                bioinfo
                Bioinformatics
                Oxford University Press
                1367-4803
                1460-2059
                1 August 2009
                19 May 2009
                19 May 2009
                : 25
                : 15
                : 1994-1996
                Affiliations
                1 Structural and Computational Biology Unit, EMBL Meyerhofstrasse 1, Heidelberg, Germany, 2 Bioinformatics & Medical Informatics Team, Biomedical Research Foundation of the Academy of Athens, Soranou Efesiou 4, GR-11527, Athens, Greece and 3 Department of Energy Joint Genome Institute (DOE-JGI), Genome Biology Program, 2800 Mitchell Drive, Walnut Creek, CA 94598, US
                Author notes
                * To whom correspondence should be addressed.

                The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors and the last two authors should be regarded as joint Last Authors.

                Associate Editor: Jonathan Wren

                Article
                btp330
                10.1093/bioinformatics/btp330
                2712340
                19454618
                f0512a01-410a-45a0-9381-b4cbe8450956
                © 2009 The Author(s)

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 16 March 2009
                : 27 April 2009
                : 15 May 2009
                Categories
                Applications Note
                Systems Biology

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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