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      A plug-and-play solution to bring network exploration and drug repurposing to biomedical web platforms

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            Abstract

            Heterogeneous biological networks are an efficient way to represent interaction systems of biomedical entities such as disease modules or drug-protein interactomes. Online resources for multi-omics analyses and other biomedical tools have to either develop a suitable network representation for their results or to omit this feature. This results in a large variety of custom solutions of different quality for network visualization and network-enhanced drug repurposing prediction. We developed Drugst.One, a customizable plug-and-play solution for biomedical web-application developers in need of a feature-rich network explorer closing the gap between gene sets or modules and drugs. Drugst.One integrates multiple databases to complement visualized data, and network algorithms for drug target identification and drug repurposing analyses. Given lists of genes, proteins or small networks, Drugst.One instantly enriches the input with biomedical information such as protein-,drug-protein interactions, and disease associations. Network algorithms enable users to conduct follow-up analyses by connecting scattered proteins, by identifying additional drug targets as well as by ranking drugs regarding their repurposing potential. The lightweight and fully customizable plug-in can be integrated into any web application with just a few lines of code that developers can generate even automatically using the Drugst.One web code generator. Its components and functions are adjustable to fit the host web pages' purpose and style. “Drugst.One” buttons allow the integration into any existing network analysis web tool without the need to integrate the plugin into the website. Drugst.One is available at https://drugst.one/ and as a python package at https://pypi.org/project/drugstone/.

            Content

            Author and article information

            Conference
            ScienceOpen
            26 August 2022
            Affiliations
            [1 ] Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany ( https://ror.org/00g30e956)
            [2 ] Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany ( https://ror.org/02kkvpp62)
            [3 ] Computational Biomedicine Lab, Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark ( https://ror.org/03yrrjy16)
            Author notes
            Author information
            https://orcid.org/0000-0002-3992-0125
            https://orcid.org/0000-0003-4408-0068
            https://orcid.org/0000-0003-4851-8848
            https://orcid.org/0000-0001-7143-7734
            https://orcid.org/0000-0002-0941-4168
            https://orcid.org/0000-0002-9424-8052
            https://orcid.org/0000-0002-0282-0462
            Article
            10.14293/S2199-1006.1.SOR-.PPPVBD8W.v1
            e50c4f93-1842-411b-b302-dd03fb757cb4

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

            RExPO22
            Maastricht, Netherlands
            2-3 September, 2022
            History
            : 26 August 2022
            Product

            ScienceOpen

            Funding
            Funded by: funder-id http://dx.doi.org/10.13039/100010661, Horizon 2020 Framework Programme;
            Award ID: 777111

            The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
            Drug Repurposing,Web Development,Web Application,Biomedical Network Exploration,Heterogeneous Networks,Biomedical Data Analysis,Data Visualisation

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