Zusammenfassung
Die aufkommende antimikrobielle Resistenz (AMR) gegenüber aktuellen antimikrobiellen Wirkstoffen ist das vordergründige öffentliche Gesundheitsproblem, das weiterhin Herausforderungen bei der Auswahl von Therapieschemata zur Behandlung von Infektionskrankheiten stellt. Die bakteriellen Krankheitserreger entwickeln AMR durch zwei Arten von Mechanismen, einer ist die intrinsische Resistenz aufgrund von Mutationen in chromosomalen Genen und der andere ist die extrinsische Resistenz durch den Erwerb von externen, plasmidvermittelten Genen. Der Schlüssel zur Diagnose von AMR liegt in der DNA-Sequenz von Bakterien, die die Resistenz verleihenden Mechanismen beherbergen. Die Fortschritte in der Technologie haben eine Fülle von Genomdaten generiert, die zur Identifizierung von diagnostischen Markern genutzt werden können. Darüber hinaus hat das maschinelle Lernen (ML) neue Möglichkeiten geschaffen, um Gesundheitsprobleme mit Hilfe von bioinformatischen Techniken signifikant zu lösen. In der letzten Dekade hat die naturinspirierte Intelligenz (NII) die Entwicklung von maschinellen Lernwerkzeugen zur Diagnose von antibakteriellen Resistenzgenmustern unterstützt. Die erfolgreiche Implementierung dieser Algorithmen, insbesondere bei komplexen und verwickelten Problemen, zeigt ihre Bedeutung in der künstlichen Intelligenz (KI). Diese Übersicht behandelt die Rolle der NII bei der Bekämpfung von Infektionskrankheiten mit Hilfe von Genomdaten sowie die zukünftige Perspektive ihrer Verwendung bei der Informationsverarbeitung, Entscheidungsfindung und Optimierung für die Diagnose von AMR. Die Schlüsselprobleme bei der praktischen Anwendung von NII mit Hilfe von genomischen Markern und mikrobiologischen Parametern werden ausführlich diskutiert. Dies wird dazu beitragen, die Lücke zwischen theoretischen Forschern, medizinischen Praktikern, Fachleuten und Ingenieuren, die an der Verwendung von NII zur Lösung von AMR interessiert sind, zu schließen.
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Sharma, P., Sethi, G., Tripathi, M.K., Rana, S., Singh, H., Kaur, P. (2024). Die Rolle der naturinspirierten Intelligenz bei der genomischen Diagnose antimikrobieller Resistenzen. In: Raza, K. (eds) Von der Natur inspirierte intelligente Datenverarbeitungstechniken in der Bioinformatik. Springer, Singapore. https://doi.org/10.1007/978-981-99-7808-3_12
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