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      DisSim: an online system for exploring significant similar diseases and exhibiting potential therapeutic drugs

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          Abstract

          The similarity of pair-wise diseases reveals the molecular relationships between them. For example, similar diseases have the potential to be treated by common therapeutic chemicals (TCs). In this paper, we introduced DisSim, an online system for exploring similar diseases, and comparing corresponding TCs. Currently, DisSim implemented five state-of-the-art methods to measure the similarity between Disease Ontology (DO) terms and provide the significance of the similarity score. Furthermore, DisSim integrated TCs of diseases from the Comparative Toxicogenomics Database (CTD), which can help to identify potential relationships between TCs and similar diseases. The system can be accessed from http://123.59.132.21:8080/DisSim.

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          Gene Ontology: tool for the unification of biology

          Michael Ashburner, Catherine A. Ball, Judith Blake … (2002)
          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            A new method to measure the semantic similarity of GO terms.

            James Wang, Zhidian Du, Rapeeporn Payattakool … (2007)
            Although controlled biochemical or biological vocabularies, such as Gene Ontology (GO) (http://www.geneontology.org), address the need for consistent descriptions of genes in different data sources, there is still no effective method to determine the functional similarities of genes based on gene annotation information from heterogeneous data sources. To address this critical need, we proposed a novel method to encode a GO term's semantics (biological meanings) into a numeric value by aggregating the semantic contributions of their ancestor terms (including this specific term) in the GO graph and, in turn, designed an algorithm to measure the semantic similarity of GO terms. Based on the semantic similarities of GO terms used for gene annotation, we designed a new algorithm to measure the functional similarity of genes. The results of using our algorithm to measure the functional similarities of genes in pathways retrieved from the saccharomyces genome database (SGD), and the outcomes of clustering these genes based on the similarity values obtained by our algorithm are shown to be consistent with human perspectives. Furthermore, we developed a set of online tools for gene similarity measurement and knowledge discovery. The online tools are available at: http://bioinformatics.clemson.edu/G-SESAME. http://bioinformatics.clemson.edu/Publication/Supplement/gsp.htm.
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              The genetic association database.

              Kevin G Becker, Kathleen C Barnes, Tiffani J Bright … (2004)
              Article 0 comments Cited 445 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Sci Rep
                Journal ID (iso-abbrev): Sci Rep
                Title: Scientific Reports
                Publisher: Nature Publishing Group
                ISSN (Electronic): 2045-2322
                Publication date (Electronic): 26 July 2016
                Publication date Collection: 2016
                Volume: 6
                Electronic Location Identifier: 30024
                Affiliations
                [1 ]College of Bioinformatics Science and Technology, Harbin Medical University , Harbin 150081, PR China
                [2 ]Hospital for Sick Children , Toronto, Canada
                [3 ]School of Management, Harbin University of Commerce , Harbin 150081, PR China
                [4 ]School of Life Science and Technology, Harbin Institute of Technology , Harbin 150081, PR China.
                Author notes
                Article
                Publisher Item ID: srep30024
                DOI: 10.1038/srep30024
                PMC ID: 4960572
                PubMed ID: 27457921
                SO-VID: 8fe0440c-e060-42a3-a2e6-0acae093e92e
                Copyright © Copyright © 2016, Macmillan Publishers Limited
                License:

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                Date received : 12 April 2016
                Date accepted : 27 June 2016
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