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      DiseaseConnect: a comprehensive web server for mechanism-based disease–disease connections

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          Abstract

          The DiseaseConnect ( http://disease-connect.org) is a web server for analysis and visualization of a comprehensive knowledge on mechanism-based disease connectivity. The traditional disease classification system groups diseases with similar clinical symptoms and phenotypic traits. Thus, diseases with entirely different pathologies could be grouped together, leading to a similar treatment design. Such problems could be avoided if diseases were classified based on their molecular mechanisms. Connecting diseases with similar pathological mechanisms could inspire novel strategies on the effective repositioning of existing drugs and therapies. Although there have been several studies attempting to generate disease connectivity networks, they have not yet utilized the enormous and rapidly growing public repositories of disease-related omics data and literature, two primary resources capable of providing insights into disease connections at an unprecedented level of detail. Our DiseaseConnect, the first public web server, integrates comprehensive omics and literature data, including a large amount of gene expression data, Genome-Wide Association Studies catalog, and text-mined knowledge, to discover disease–disease connectivity via common molecular mechanisms. Moreover, the clinical comorbidity data and a comprehensive compilation of known drug–disease relationships are additionally utilized for advancing the understanding of the disease landscape and for facilitating the mechanism-based development of new drug treatments.

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          Most cited references29

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          Cytoscape Web: an interactive web-based network browser

          Summary: Cytoscape Web is a web-based network visualization tool–modeled after Cytoscape–which is open source, interactive, customizable and easily integrated into web sites. Multiple file exchange formats can be used to load data into Cytoscape Web, including GraphML, XGMML and SIF. Availability and Implementation: Cytoscape Web is implemented in Flex/ActionScript with a JavaScript API and is freely available at http://cytoscapeweb.cytoscape.org/ Contact: gary.bader@utoronto.ca Supplementary information: Supplementary data are available at Bioinformatics online.
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            A text-mining analysis of the human phenome.

            A number of large-scale efforts are underway to define the relationships between genes and proteins in various species. But, few attempts have been made to systematically classify all such relationships at the phenotype level. Also, it is unknown whether such a phenotype map would carry biologically meaningful information. We have used text mining to classify over 5000 human phenotypes contained in the Online Mendelian Inheritance in Man database. We find that similarity between phenotypes reflects biological modules of interacting functionally related genes. These similarities are positively correlated with a number of measures of gene function, including relatedness at the level of protein sequence, protein motifs, functional annotation, and direct protein-protein interaction. Phenotype grouping reflects the modular nature of human disease genetics. Thus, phenotype mapping may be used to predict candidate genes for diseases as well as functional relations between genes and proteins. Such predictions will further improve if a unified system of phenotype descriptors is developed. The phenotype similarity data are accessible through a web interface at http://www.cmbi.ru.nl/MimMiner/.
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              The implications of human metabolic network topology for disease comorbidity.

              Most diseases are the consequence of the breakdown of cellular processes, but the relationships among genetic/epigenetic defects, the molecular interaction networks underlying them, and the disease phenotypes remain poorly understood. To gain insights into such relationships, here we constructed a bipartite human disease association network in which nodes are diseases and two diseases are linked if mutated enzymes associated with them catalyze adjacent metabolic reactions. We find that connected disease pairs display higher correlated reaction flux rate, corresponding enzyme-encoding gene coexpression, and higher comorbidity than those that have no metabolic link between them. Furthermore, the more connected a disease is to other diseases, the higher is its prevalence and associated mortality rate. The network topology-based approach also helps to uncover potential mechanisms that contribute to their shared pathophysiology. Thus, the structure and modeled function of the human metabolic network can provide insights into disease comorbidity, with potentially important consequences for disease diagnosis and prevention.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                01 July 2014
                03 June 2014
                03 June 2014
                : 42
                : Web Server issue
                : W137-W146
                Affiliations
                [1 ]Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung 402, Taiwan
                [2 ]Agricultural Biotechnology Center, National Chung Hsing University, Taichung 402, Taiwan
                [3 ]Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
                [4 ]Computation Institute, Institute for Genomics and Systems Biology University of Chicago, Chicago, IL 60637, USA
                [5 ]Institute of Molecular Biology, National Chung Hsing University, Taichung 402, Taiwan
                [6 ]Institute of Biomedical Sciences, National Chung Hsing University, Taichung 402, Taiwan
                [7 ]Jane Anne Nohl Division of Hematology and Center for the Study of Blood Diseases, University of Southern California Keck School of Medicine, Los Angeles, CA 90033, USA
                [8 ]Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
                [9 ]Will Rogers Institute Pulmonary Research Center, University of Southern California, Los Angeles, CA 90033, USA
                [10 ]Department of Medicine, University of Southern California, Los Angeles, CA 90089, USA
                Author notes
                [* ]To whom correspondence should be addressed. Tel: +1 213 740 7055; Fax: +1 213 740 2475; Email: xjzhou@ 123456usc.edu
                Correspondence may also be addressed to Chun-Chi Liu. Tel: +886 4 22840338 (Ext 7031); Fax: +886 4 22859329; Email: jimliu@ 123456nchu.edu.tw
                Article
                10.1093/nar/gku412
                4086092
                24895436
                bd0f6d98-5872-496e-a721-2b9ec2da13fd
                © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 29 April 2014
                : 19 April 2014
                : 03 March 2014
                Page count
                Pages: 11
                Categories
                Article
                Custom metadata
                1 July 2014

                Genetics
                Genetics

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