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      From genes to drugs with a feature-rich web plugin

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            Abstract

            Network medicine is aiming to model and extract knowledge using network or graph structures to represent relationships between biological entities such as diseases, proteins, or drugs. These heterogeneous networks can be used to craft interpretable visualizations of biological interactions [1,2], to find and extract disease mechanisms [3], and to identify drug repurposing candidates [4,5]. We developed Drugst.One [6] a feature-rich web plugin, designed to streamline and facilitate the development and accessibility of network medicine applications and techniques.At its core, the Drugst.One Plugin is a highly customizable web plugin, that can be adjusted to any hosts web-page’s functional or stylistic requirements. It can be modified through an interactive configuration creator on the project page. Handed a set of genes or proteins, either manually or dynamically though scripts on the website, Drugst.One visualizes them in their network context. Users can then explore the network interactively or enrich the network with related drugs, diseases, or launch algorithms to identify disease modules or drug repurposing candidates with only one click. A Drugst.One server, provides JavaScript files for integration of the plugin, the execution of network algorithms, and a data warehousing system, which maintains multiple established protein- and drug-interaction database integrations and weekly updates most for some of them weekly.A ready-to-use version of Drugst.One is integrated into the homepage, usable through both, manual entering of entities of interest or HTTP-request-driven configuration, removing the immediate need to host an own specialized website. We further offer a Python package, which to provides a purely programmatic access to all Drugst.One functions and offer templates for different web frameworks to efficiently and easily set up a personalized Drugst.One-powered web tool.

            Content

            Author and article information

            Conference
            RExPO24 Conference
            REPO4EU
            3 May 2024
            Affiliations
            [1 ] Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany;
            [2 ] Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany ( https://ror.org/00g30e956)
            [3 ] Data Science in Systems Biology, TUM School of Life Sciences, Technical University of Munich, Munich, Germany ( https://ror.org/02kkvpp62)
            [4 ] 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/0000-0003-4408-0068
            https://orcid.org/0000-0002-3992-0125
            https://orcid.org/0000-0002-0941-4168
            https://orcid.org/0000-0002-9424-8052
            https://orcid.org/0000-0002-0282-0462
            Article
            10.58647/REXPO.24000062.v1
            adc5538e-5533-40e1-bb89-d503b01c4cc7

            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 .

            RExPO24
            3
            Munich, Germany
            3-5 July 2024
            History
            : 3 May 2024
            Product

            REPO4EU

            Funding
            Funded by: funder-id http://dx.doi.org/10.13039/501100000780, European Commission;
            Award ID: 777111
            Award ID: 101057619
            Funded by: funder-id http://dx.doi.org/10.13039/501100002347, Bundesministerium für Bildung und Forschung;
            Award ID: F031L0214A
            Award ID: 161L0214A
            Award ID: 16LW0243K
            Funded by: funder-id http://dx.doi.org/10.13039/100008398, Villum Fonden;
            Award ID: 13154
            Categories

            All data generated or analysed during this study are included in this published article (and its supplementary information files).
            Bioinformatics & Computational biology
            Drug repurposing,network medicine,data integration ,disease module identification,web-development,interactive network exploration

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