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      • Record: found
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      Boolean implication networks derived from large scale, whole genome microarray datasets

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          A method for analysis of microarray data is presented that extracts statistically significant Boolean implication relationships between pairs of genes.

          Abstract

          We describe a method for extracting Boolean implications (if-then relationships) in very large amounts of gene expression microarray data. A meta-analysis of data from thousands of microarrays for humans, mice, and fruit flies finds millions of implication relationships between genes that would be missed by other methods. These relationships capture gender differences, tissue differences, development, and differentiation. New relationships are discovered that are preserved across all three species.

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          Emergence of scaling in random networks

          Reka Albert, Albert-László Barabási (2000)
          Systems as diverse as genetic networks or the world wide web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature is found to be a consequence of the two generic mechanisms that networks expand continuously by the addition of new vertices, and new vertices attach preferentially to already well connected sites. A model based on these two ingredients reproduces the observed stationary scale-free distributions, indicating that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
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            DAVID: Database for Annotation, Visualization, and Integrated Discovery.

            Glynn Dennis, Brad T. Sherman, Douglas A Hosack … (2003)
            Functional annotation of differentially expressed genes is a necessary and critical step in the analysis of microarray data. The distributed nature of biological knowledge frequently requires researchers to navigate through numerous web-accessible databases gathering information one gene at a time. A more judicious approach is to provide query-based access to an integrated database that disseminates biologically rich information across large datasets and displays graphic summaries of functional information. Database for Annotation, Visualization, and Integrated Discovery (DAVID; http://www.david.niaid.nih.gov) addresses this need via four web-based analysis modules: 1) Annotation Tool - rapidly appends descriptive data from several public databases to lists of genes; 2) GoCharts - assigns genes to Gene Ontology functional categories based on user selected classifications and term specificity level; 3) KeggCharts - assigns genes to KEGG metabolic processes and enables users to view genes in the context of biochemical pathway maps; and 4) DomainCharts - groups genes according to PFAM conserved protein domains. Analysis results and graphical displays remain dynamically linked to primary data and external data repositories, thereby furnishing in-depth as well as broad-based data coverage. The functionality provided by DAVID accelerates the analysis of genome-scale datasets by facilitating the transition from data collection to biological meaning.
            Article 0 comments Cited 1427 times – based on 0 reviews     Review now
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              A gene-coexpression network for global discovery of conserved genetic modules.

              Joshua M. Stuart, Eran Segal, Daphne Koller … (2003)
              To elucidate gene function on a global scale, we identified pairs of genes that are coexpressed over 3182 DNA microarrays from humans, flies, worms, and yeast. We found 22,163 such coexpression relationships, each of which has been conserved across evolution. This conservation implies that the coexpression of these gene pairs confers a selective advantage and therefore that these genes are functionally related. Many of these relationships provide strong evidence for the involvement of new genes in core biological functions such as the cell cycle, secretion, and protein expression. We experimentally confirmed the predictions implied by some of these links and identified cell proliferation functions for several genes. By assembling these links into a gene-coexpression network, we found several components that were animal-specific as well as interrelationships between newly evolved and ancient modules.
              Article 0 comments Cited 812 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Genome Biol
                Title: Genome Biology
                Publisher: BioMed Central
                ISSN (Print): 1465-6906
                ISSN (Electronic): 1465-6914
                Publication date (Print): 2008
                Publication date (Electronic): 30 October 2008
                Volume: 9
                Issue: 10
                Page: R157
                Affiliations
                [1 ]Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
                [2 ]Department of Computer Science, Stanford University, Stanford, CA 94305, USA
                [3 ]Department of Radiology, Stanford University, Stanford, CA 94305, USA
                [4 ]Department of Health Research and Policy and Department of Statistics, Stanford University, Stanford, CA 94305, USA
                Article
                Publisher ID: gb-2008-9-10-r157
                DOI: 10.1186/gb-2008-9-10-r157
                PMC ID: 2760884
                PubMed ID: 18973690
                SO-VID: b62a7ffc-9a5f-4ce0-8544-b89c031f8515
                Copyright © Copyright © 2008 Sahoo et al.; licensee BioMed Central Ltd.
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 28 June 2008
                Date revision received : 6 September 2008
                Date accepted : 30 October 2008
                Categories
                Subject: Method

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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