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      Streamlining and generalizing network-based drug discovery with NeDRex and Drugst.One

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            Abstract

            NeDRex (Network-based Drug Repurposing Explorer) (Sadegh et al. 2021, 2022) is a network-based computational tool used for drug repurposing. The NeDRex database houses a rich collection of data on Protein-Protein and Drug-Protein (target) interactions and integrates diverse data sources. The outcome is a comprehensive knowledge graph, enabling researchers to delve deep into the intricacies of these interactions. NeDRex utilizes network-based algorithms to analyze and identify potential drug-disease associations by integrating various biological data sources, such as protein-protein interaction networks, gene expression data, and drug-target interactions. Equipped with a suite of algorithms and tools, NeDRex offers capabilities for module identification, drug ranking, and intricate connectivity search, all tailored to streamline the drug repurposing journey.

            Drugst.One (Maier et al. 2023), on the other hand, emerges as a platform crafted to facilitate the integration and customization of interactive network visualizations in the fields of drug discovery and repurposing. A pivotal feature is the web plugin, which offers seamless integration into any webpage, thereby enhancing the scope of interactive drug-related visualizations.

            In essence, both NeDRex and Drugst.One highlight the significance of robust tools in drug discovery. While NeDRex centers on the analytical side with its algorithms and tools for module identification and drug ranking, Drugst.One prioritizes the user experience, offering seamless navigation and interactive visualizations. Combined, they represent valuable resources in the field of drug discovery and repurposing, simplifying the process and optimizing research efforts.

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            Author and article information

            Conference
            REPO4EU
            12 September 2023
            Affiliations
            [1 ] Institute for Computational Systems Biology, University of Hamburg, Hamburg (Germany) ( https://ror.org/00g30e956)
            [2 ] Chair of Experimental Bioinformatics, Technical University of Munich (Germany) ( https://ror.org/02kkvpp62)
            [3 ] School of Computing, Newcastle University (United Kingdom) ( https://ror.org/01kj2bm70)
            [4 ] School of Computing, Newcastle University (United Kingdom);
            [5 ] Biomedical Network Science Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnbeg (Germany) ( https://ror.org/00f7hpc57)
            [6 ] Natural Sciences Department, Universidad Autónoma Metropolitana-Cuajimalpa (Mexico) ( https://ror.org/02kta5139)
            [7 ] Division Data Science in Biomedicina, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School (Germany) ( https://ror.org/010nsgg66)
            Author notes
            Author information
            https://orcid.org/0000-0002-6171-1215
            Article
            10.58647/REXPO.23000014.v1
            1ba716bb-4657-472d-ba40-cbd3d9ca3fdc

            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 .

            RExPO23
            2
            Stockholm, Sweden
            25-26 October 2023
            History
            : 12 September 2023
            Categories

            Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
            Medicine,Databases,Computer science,Bioinformatics & Computational biology,Pharmacology & Pharmaceutical medicine
            Disease module identification,Systems medicine,Knowledge graph mining,Network-based drug repurposing,Network visualization

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