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      Integrated Tools for Enhancing Network-based Disease Module Identification, Drug Prioritization and Repurposing



            The domain of network-based drug repurposing research encounters considerable challenges in disease module identification and evaluation, further complicated by the absence of a gold standard for assessment and the requirement for user-friendly tools. The identification process involves extracting interactome genes with contextual similarities to a predefined condition-specific gene set, a process highly dependent on the use case and algorithm selected. The current tools often exhibit minimal overlap due to varied foundational assumptions, leading researchers to select tools without comprehensive suitability assessment, resulting in a preference for tools yielding expected results but potentially failing to uncover new, actionable drug repurposing candidates.

            To address the lack of robustness and the bias in tool selection, we introduce CAMI (Consensus Active Module Identification), a consensus approach suite combining existing algorithms like DOMINO [1], DIAMOnD [2], and ROBUST [3] while permitting further extensions. The ensemble classifier in CAMI is adaptable and, by default, rivals the performance of the best base tool across multiple scenarios. We assessed its efficacy using the seed rediscovery rate, a metric computed by sequentially removing a fraction of input genes (seeds) and determining the ratio of discovered, excluded genes to the module size.

            Indirect performance metrics are essential for module evaluation. Hence, we developed DIGEST, a Python-based tool for assessing disease module coherence that streamlines the in silico validation process via fully automated validation pipelines [4]. These encompass disease and gene ID mapping, enrichment analysis, shared gene and variant comparisons, and background distribution estimation, enabling users to evaluate the statistical significance of candidate modules or extracted connected components serving as candidate mechanisms concerning functional and genetic coherence and to effortlessly compute empirical P-values.

            In summary, CAMI and DIGEST, accessible as Python packages, provide an all-encompassing solution for network-based drug repurposing research. They deliver a robust approach for disease module identification and validation, diminishing tool selection bias and facilitating indirect performance assessment. While DIGEST is available as a web tool ( https://digest-validation.net/ ), a CAMI web tool is currently under development.


            Author and article information

            RExPO23 Conference
            23 September 2023
            [1 ] Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany ( https://ror.org/00g30e956)
            [2 ] Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany;
            [3 ] Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany;
            [4 ] Computational Biomedicine Lab, Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark ( https://ror.org/03yrrjy16)
            Author notes
            Author information

            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 .

            Stockholm, Sweden
            25-26 October 2023
            : 23 September 2023
            Funded by: funder-id http://dx.doi.org/10.13039/501100007601, Horizon 2020;
            Award ID: 777111
            Funded by: funder-id http://dx.doi.org/10.13039/100018696, HORIZON EUROPE Health;
            Award ID: 101057619
            Funded by: funder-id http://dx.doi.org/10.13039/501100002347, Bundesministerium für Bildung und Forschung;
            Award ID: FKZ161L0214A
            Funded by: funder-id http://dx.doi.org/10.13039/100008398, Villum Fonden;
            Award ID: 13154

            The datasets generated during and/or analysed during the current study are available in the repository: https://gitlab.rrz.uni-hamburg.de/bay2046/cami, https://github.com/bionetslab/digest
            Bioinformatics & Computational biology
            network-based drug repurposing,disease module detection,CAMI,DIGEST,consensus approach,in silico validation


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