Now showing 1 - 2 of 2
  • Publication
    A machine learning method for the identification and characterization of novel COVID-19 drug targets
    ( 2023-05-03) ;
    Delong, Lauren Nicole
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    Masny, Aliaksandr
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    Lentzen, Manuel
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    Dijk, David van
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    Hansen, Anne Funck
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    Kannt, Aimo
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    Foldenauer, Ann Christina
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    Resch, Eduard
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    Frank, Kevin
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    Laue, Hendrik
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    Hirsch, Jochen
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    Wischnewski, Marco
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    Tom Kodamullil, Alpha
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    Gemünd, Andre
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    Fluck, Juliane
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    Steinborn, Carina
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    Hermanowski, Helena
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    Klein, Jürgen
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    Knieps, Meike
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    Wendland, Philipp Johannes
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    Wegner, Philipp
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    Lentzen, Manuel
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    In addition to vaccines, the World Health Organization sees novel medications as an urgent matter to fight the ongoing COVID-19 pandemic. One possible strategy is to identify target proteins, for which a perturbation by an existing compound is likely to benefit COVID-19 patients. In order to contribute to this effort, we present GuiltyTargets-COVID-19 (https://guiltytargets-covid.eu/), a machine learning supported web tool to identify novel candidate drug targets. Using six bulk and three single cell RNA-Seq datasets, together with a lung tissue specific protein-protein interaction network, we demonstrate that GuiltyTargets-COVID-19 is capable of (i) prioritizing meaningful target candidates and assessing their druggability, (ii) unraveling their linkage to known disease mechanisms, (iii) mapping ligands from the ChEMBL database to the identified targets, and (iv) pointing out potential side effects in the case that the mapped ligands correspond to approved drugs. Our example analyses identified 4 potential drug targets from the datasets: AKT3 from both the bulk and single cell RNA-Seq data as well as AKT2, MLKL, and MAPK11 in the single cell experiments. Altogether, we believe that our web tool will facilitate future target identification and drug development for COVID-19, notably in a cell type and tissue specific manner.
  • Publication
    The technologically integrated oncosimulator: Combining multiscale cancer modeling with information technology in the in silico oncology context
    ( 2014)
    Stamatakos, Georgios
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    Dionysiou, Dimitra
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    Lunzer, Aran
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    Belleman, Robert
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    Kolokotroni, Eleni
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    Georgiadi, Eleni
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    Pukacki, Juliusz
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    Giatili, Stavroula
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    d`Onofrio, Alberto
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    Sfakianakis, Stelios
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    Marias, Kostas
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    Desmedt, Christine
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    Tsiknakis, Manolis
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    Graf, Norbert
    This paper outlines the major components and function of the Technologically Integrated Oncosimulator developed primarily within the ACGT (Advancing Clinico Genomic Trials on Cancer) project. The Oncosimulator is defined as an information technology system simulating in vivo tumor response to therapeutic modalities within the clinical trial context. Chemotherapy in the neoadjuvant setting, according to two real clinical trials concerning nephroblastoma and breast cancer, has been considered. The spatiotemporal simulation module embedded in the Oncosimulator is based on the multiscale, predominantly top-down, discrete entity - discrete event cancer simulation technique developed by the In Silico Oncology Group, National Technical University of Athens. The technology modules include multiscale data handling, image processing, invocation of code execution via a spreadsheet-inspired environment portal, execution of the code on the grid and visualization of the predictions. A refining scenario for the eventual coupling of the Oncosimulator with immunological models is also presented. Parameter values have been adapted to multiscale clinical trial data in a consistent way, thus supporting the predictive potential of the Oncosimulator. Indicative results demonstrating various aspects of the clinical adaptation and validation process are presented. Completion of these processes is expected to pave the way for the clinical translation of the system.