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      BoCluSt: Bootstrap Clustering Stability Algorithm for Community Detection

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      PLoS ONE
      Public Library of Science

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          Abstract

          The identification of modules or communities in sets of related variables is a key step in the analysis and modeling of biological systems. Procedures for this identification are usually designed to allow fast analyses of very large datasets and may produce suboptimal results when these sets are of a small to moderate size. This article introduces BoCluSt, a new, somewhat more computationally intensive, community detection procedure that is based on combining a clustering algorithm with a measure of stability under bootstrap resampling. Both computer simulation and analyses of experimental data showed that BoCluSt can outperform current procedures in the identification of multiple modules in data sets with a moderate number of variables. In addition, the procedure provides users with a null distribution of results to evaluate the support for the existence of community structure in the data. BoCluSt takes individual measures for a set of variables as input, and may be a valuable and robust exploratory tool of network analysis, as it provides 1) an estimation of the best partition of variables into modules, 2) a measure of the support for the existence of modular structures, and 3) an overall description of the whole structure, which may reveal hierarchical modular situations, in which modules are composed of smaller sub-modules.

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          Most cited references13

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          Hierarchical organization of modularity in metabolic networks

          Spatially or chemically isolated functional modules composed of several cellular components and carrying discrete functions are considered fundamental building blocks of cellular organization, but their presence in highly integrated biochemical networks lacks quantitative support. Here we show that the metabolic networks of 43 distinct organisms are organized into many small, highly connected topologic modules that combine in a hierarchical manner into larger, less cohesive units, their number and degree of clustering following a power law. Within Escherichia coli the uncovered hierarchical modularity closely overlaps with known metabolic functions. The identified network architecture may be generic to system-level cellular organization.
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            Maps of random walks on complex networks reveal community structure

            To comprehend the multipartite organization of large-scale biological and social systems, we introduce a new information theoretic approach that reveals community structure in weighted and directed networks. The method decomposes a network into modules by optimally compressing a description of information flows on the network. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of more than 6000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network -- including physics, chemistry, molecular biology, and medicine -- information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences.
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              Functional cartography of complex metabolic networks

              , (2005)
              High-throughput techniques are leading to an explosive growth in the size of biological databases and creating the opportunity to revolutionize our understanding of life and disease. Interpretation of these data remains, however, a major scientific challenge. Here, we propose a methodology that enables us to extract and display information contained in complex networks. Specifically, we demonstrate that one can (i) find functional modules in complex networks, and (ii) classify nodes into universal roles according to their pattern of intra- and inter-module connections. The method thus yields a ``cartographic representation'' of complex networks. Metabolic networks are among the most challenging biological networks and, arguably, the ones with more potential for immediate applicability. We use our method to analyze the metabolic networks of twelve organisms from three different super-kingdoms. We find that, typically, 80% of the nodes are only connected to other nodes within their respective modules, and that nodes with different roles are affected by different evolutionary constraints and pressures. Remarkably, we find that low-degree metabolites that connect different modules are more conserved than hubs whose links are mostly within a single module.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                3 June 2016
                2016
                : 11
                : 6
                : e0156576
                Affiliations
                [001]CIBUS Universidade de Santiago, Campus Sur, 15782 Santiago de Compostela, Galiza, Spain
                University of Ulm, GERMANY
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: CG. Performed the experiments: CG. Analyzed the data: CG. Contributed reagents/materials/analysis tools: CG. Wrote the paper: CG.

                Article
                PONE-D-15-34211
                10.1371/journal.pone.0156576
                4892581
                27258041
                679c7111-9a9a-41ce-894e-d5f225f754dc
                © 2016 Carlos Garcia

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 4 August 2015
                : 17 May 2016
                Page count
                Figures: 4, Tables: 2, Pages: 15
                Funding
                Funded by: Ministerio of Economía and Competitividad
                Award ID: CGL2012-39861-C02-01
                Award Recipient :
                This work was funded by Ministerio de Economía and Competitividad (URL: www.mineco.gob.es/; grant number: CGL2012-39861-C02-01). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Physical Sciences
                Mathematics
                Applied Mathematics
                Algorithms
                Research and Analysis Methods
                Simulation and Modeling
                Algorithms
                Biology and Life Sciences
                Cell Biology
                Cell Processes
                Cell Growth
                Computer and Information Sciences
                Computer Modeling
                Biology and Life Sciences
                Genetics
                Gene Expression
                Biology and life sciences
                Genetics
                DNA
                DNA repair
                Biology and life sciences
                Biochemistry
                Nucleic acids
                DNA
                DNA repair
                Computer and Information Sciences
                Network Analysis
                Biology and Life Sciences
                Genetics
                Gene Expression
                Gene Regulation
                Physical Sciences
                Mathematics
                Algebra
                Linear Algebra
                Eigenvectors
                Custom metadata
                Data are available from the NCBI database, accession GSE24729 ( http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE24729).

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