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Wissensgenerierung aus komplexen Datensätzen in der humanexperimentellen Schmerzforschung

Generating knowledge from complex data sets in human experimental pain research

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Zusammenfassung

Schmerz hat eine komplexe Pathophysiologie, die sich in komplexen und heterogenen klinischen Phänotypen ausdrückt. Dies macht die Erforschung von Schmerz und seiner Behandlung zu einem potenziell datenintensiven Thema, bei dessen Bearbeitung große Mengen komplexer Daten aufgenommen werden. Typische Quellen solcher Daten sind Untersuchungen mit funktioneller Magnetresonanztomographie, komplexen quantitativ-sensorischen Tests, DNA-Sequenzierung, insbesondere dem sog. „next generation sequencing“, oder funktionell-genomischen Forschungsansätzen wie zum Beispiel solchen, die auf die Entdeckung oder Repositionierung von bekannten Arzneimitteln als neue Analgetika gerichtet sind. Die Extraktion von Informationen aus solchen Big Data erfordert datenwissenschaftliche Methoden, die der Informatik mehr als der Statistik zuzuordnen sind. Derzeit richtet sich ein besonderes Interesse auf das maschinelle Lernen, welches Methoden zur Detektion interessanter, insbesondere biologisch aussagekräftiger Strukturen in hochdimensionalen Daten bereitstellt, um sog. Klassifikatoren zu erstellen, die klinische Phänotypen z. B. aus klinischen oder genetischen Merkmalen vorhersagen. Darüber hinaus können diese Methoden zur Wissensentdeckung in großen aus biomedizinischen Datenbanken ausgelesenen Datensätzen verwendet werden, um Hypothesen zu generieren und das derzeitige Wissen über Schmerz zur Entwicklung neuer Analgetika zu nutzen. Dies ermöglicht, in der Schmerzforschung sogenannte DIKW-Ansätze (Daten – Information – Wissen [„knowledge“] – Weisheit) zu verfolgen. In diesem Artikel wird anhand aktueller Beispiele aus der Schmerzforschung ein Überblick über die aktuellen datenwissenschaftlichen Methoden in diesem Forschungsbereich vermittelt.

Abstract

Pain has a complex pathophysiology that is expressed in multifaceted and heterogeneous clinical phenotypes. This makes research on pain and its treatment a potentially data-rich field as large amounts of complex data are generated. Typical sources of such data are investigations with functional magnetic resonance imaging, complex quantitative sensory testing, next-generation DNA sequencing and functional genomic research approaches, such as those aimed at analgesic drug discovery or repositioning of drugs known from other indications as new analgesics. Extracting information from these big data requires complex data scientific-based methods belonging more to computer science than to statistics. A particular interest is currently focused on machine learning, the methods of which are used for the detection of interesting and biologically meaningful structures in high-dimensional data. Subsequently, classifiers can be created that predict clinical phenotypes from, e.g. clinical or genetic features acquired from subjects. In addition, knowledge discovery in big data accessible in electronic knowledge bases, can be used to generate hypotheses and to exploit the accumulated knowledge about pain for the discovery of new analgesic drugs. This enables so-called data—information—knowledge—wisdom (DIKW) approaches to be followed in pain research. This article highlights current examples from pain research to provide an overview about contemporary data scientific methods used in this field of research.

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Förderung

Landesoffensive zur Entwicklung wissenschaftlich-ökonomischer Exzellenz (LOEWE), LOEWE-Zentrum für Translationale Medizin und Pharmakologie (GG, JL)

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Authors and Affiliations

  1. Institut für Klinische Pharmakologie, Goethe-Universität, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Deutschland

    Jörn Lötsch &  Gerd Geisslinger

  2. Institutsteil Translationale Medizin und Pharmakologie (TMP), Fraunhofer-Institut für Molekularbiologie und Angewandte Oekologie (IME), Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Deutschland

    Jörn Lötsch,  Gerd Geisslinger &  Carmen Walter

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  1. Jörn Lötsch
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Correspondence to Jörn Lötsch.

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Interessenkonflikt

J. Lötsch, G. Geisslinger und C. Walter geben an, dass kein Interessenkonflikt besteht.

Für diesen Beitrag wurden von den Autoren keine Studien an Menschen oder Tieren durchgeführt. Für die aufgeführten Studien gelten die jeweils dort angegebenen ethischen Richtlinien.

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Lötsch, J., Geisslinger, G. & Walter, C. Wissensgenerierung aus komplexen Datensätzen in der humanexperimentellen Schmerzforschung. Schmerz 33, 502–513 (2019). https://doi.org/10.1007/s00482-019-00412-5

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  • DOI: https://doi.org/10.1007/s00482-019-00412-5

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