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      The good, the bad, and the ugly: Drug response prediction using machine learning

      Published
      conference-abstract
      1 , 1 , 1 , 1
      REPO4EU
      RExPO24
      3-5 July 2024
      Drug response prediction, reproducibility, generalization, robustness, machine learning, deep learning, foundational model, proteomics
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            Abstract

            Content

            Author and article information

            Conference
            REPO4EU
            8 May 2024
            Affiliations
            [1 ] Technical University of Munich ( https://ror.org/02kkvpp62)
            Author information
            https://orcid.org/0000-0003-0428-1703
            https://orcid.org/0000-0001-5812-8013
            https://orcid.org/0000-0002-0941-4168
            https://orcid.org/0000-0002-9224-3258
            Article
            10.58647/REXPO.24004
            7e09dbc2-8e8e-4099-8da2-0a15d209c03d
            The Authors

            Published under Creative Commons Attribution 4.0 International ( CC BY 4.0). Users are allowed to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material for any purpose, even commercially), as long as the authors and the publisher are explicitly identified and properly acknowledged as the original source.

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

            REPO4EU

            Categories

            Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
            Pharmacology & Pharmaceutical medicine
            Drug response prediction,reproducibility,generalization,robustness,machine learning,deep learning,foundational model,proteomics

            References

            1. Lautenbacher Ludwig, Samaras Patroklos, Muller Julian, Grafberger Andreas, Shraideh Marwin, Rank Johannes, Fuchs Simon T, Schmidt Tobias K, The Matthew, Dallago Christian, Wittges Holger, Rost Burkhard, Krcmar Helmut, Kuster Bernhard, Wilhelm Mathias. ProteomicsDB: toward a FAIR open-source resource for life-science research. Nucleic Acids Research. Vol. 50(D1)2022. Oxford University Press (OUP). [Cross Ref]

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