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Challenges of Multiplex Assays for COVID-19 Research: A Machine Learning Perspective

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Multiplex Biomarker Techniques

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2511))

Abstract

Multiplex assays that provide simultaneous measurement of multiple analytes in biological samples have now developed into widely used technologies in the study of diseases, drug discovery, and other medical areas. These approaches span multiple assay systems and can provide readouts of specific assay components with similar accuracy as the respective single assay measurements. Multiplexing allows the consumption of lower sample volumes, lower costs, and higher throughput compared with carrying out single assays. A number of recent studies have demonstrated the impact of multiplex assays in the study of the SARS-CoV-2 virus, the infectious agent responsible for the current COVID-19 pandemic. In this respect, machine learning techniques have proven to be highly valuable in capturing complex disease phenotypes and converting these insights into models which can be applied in real-world settings. This chapter gives an overview of opportunities and challenges of multiplexed biomarker analysis, with a focus on the use of machine learning aimed at identification of biological signatures for increasing our understanding of COVID-19 disease, and for improved diagnostics and prediction of disease outcomes.

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Authors and Affiliations

  1. Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil

    Paul C. Guest

  2. Section of Forensic Psychiatry, Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany

    David Popovic

  3. International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany

    David Popovic

  4. Laboratory of Translational Psychiatry, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany

    Johann Steiner

  5. Department of Psychiatry, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany

    Johann Steiner

  6. Center for Behavioral Brain Sciences, Magdeburg, Germany

    Johann Steiner

  7. German Center for Mental Health (DZP), Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Site Jena-Magdeburg-Halle, Magdeburg, Germany

    Johann Steiner

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  1. Paul C. Guest
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  2. David Popovic
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Editors and Affiliations

  1. Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, University of Campinas, Campinas, Sao Paulo, Brazil

    Paul C. Guest

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Guest, P.C., Popovic, D., Steiner, J. (2022). Challenges of Multiplex Assays for COVID-19 Research: A Machine Learning Perspective. In: Guest, P.C. (eds) Multiplex Biomarker Techniques. Methods in Molecular Biology, vol 2511. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2395-4_3

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  • DOI: https://doi.org/10.1007/978-1-0716-2395-4_3

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