Literature DB >> 15374873

Iterative cluster analysis of protein interaction data.

Vicente Arnau1, Sergio Mars, Ignacio Marín.   

Abstract

MOTIVATION: Generation of fast tools of hierarchical clustering to be applied when distances among elements of a set are constrained, causing frequent distance ties, as happens in protein interaction data.
RESULTS: We present in this work the program UVCLUSTER, that iteratively explores distance datasets using hierarchical clustering. Once the user selects a group of proteins, UVCLUSTER converts the set of primary distances among them (i.e. the minimum number of steps, or interactions, required to connect two proteins) into secondary distances that measure the strength of the connection between each pair of proteins when the interactions for all the proteins in the group are considered. We show that this novel strategy has advantages over conventional clustering methods to explore protein-protein interaction data. UVCLUSTER easily incorporates the information of the largest available interaction datasets to generate comprehensive primary distance tables. The versatility, simplicity of use and high speed of UVCLUSTER on standard personal computers suggest that it can be a benchmark analytical tool for interactome data analysis. AVAILABILITY: The program is available upon request from the authors, free for academic users. Additional information available at http://www.uv.es/genomica/UVCLUSTER.

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Year:  2004        PMID: 15374873     DOI: 10.1093/bioinformatics/bti021

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


  46 in total

1.  Evaluation of clustering algorithms for protein-protein interaction networks.

Authors:  Sylvain Brohée; Jacques van Helden
Journal:  BMC Bioinformatics       Date:  2006-11-06       Impact factor: 3.169

2.  Protein complex prediction based on k-connected subgraphs in protein interaction network.

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3.  Recent advances in clustering methods for protein interaction networks.

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5.  How frequently do clusters occur in hierarchical clustering analysis? A graph theoretical approach to studying ties in proximity.

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6.  Chapter 5: Network biology approach to complex diseases.

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7.  Surprise maximization reveals the community structure of complex networks.

Authors:  Rodrigo Aldecoa; Ignacio Marín
Journal:  Sci Rep       Date:  2013-01-14       Impact factor: 4.379

8.  RRW: repeated random walks on genome-scale protein networks for local cluster discovery.

Authors:  Kathy Macropol; Tolga Can; Ambuj K Singh
Journal:  BMC Bioinformatics       Date:  2009-09-09       Impact factor: 3.169

9.  Interactome and Gene Ontology provide congruent yet subtly different views of a eukaryotic cell.

Authors:  Antonio Marco; Ignacio Marín
Journal:  BMC Syst Biol       Date:  2009-07-15

10.  A knowledge-based decision support system in bioinformatics: an application to protein complex extraction.

Authors:  Antonino Fiannaca; Massimo La Rosa; Alfonso Urso; Riccardo Rizzo; Salvatore Gaglio
Journal:  BMC Bioinformatics       Date:  2013-01-14       Impact factor: 3.169

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