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Addressing antibiotic resistance: computational answers to a biological problem?
Behling, Anna H; Wilson, Brooke C; Ho, Daniel; Virta, Marko; O'Sullivan, Justin M; Vatanen, Tommi.
Afiliación
  • Behling AH; Liggins Institute, University of Auckland, Auckland, New Zealand.
  • Wilson BC; Liggins Institute, University of Auckland, Auckland, New Zealand.
  • Ho D; Liggins Institute, University of Auckland, Auckland, New Zealand.
  • Virta M; Department of Microbiology, University of Helsinki, Helsinki, Finland.
  • O'Sullivan JM; Liggins Institute, University of Auckland, Auckland, New Zealand; The Maurice Wilkins Centre, The University of Auckland, Private Bag 92019, Auckland, New Zealand; Australian Parkinsons Mission, Garvan Institute of Medical Research, Sydney, New South Wales, 384 Victoria Street, Darlinghurst, NSW 201
  • Vatanen T; Liggins Institute, University of Auckland, Auckland, New Zealand; Department of Microbiology, University of Helsinki, Helsinki, Finland; Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Broad Institute of MIT and Harvard, Cambrid
Curr Opin Microbiol ; 74: 102305, 2023 08.
Article en En | MEDLINE | ID: mdl-37031568
The increasing prevalence of infections caused by antibiotic-resistant bacteria is a global healthcare crisis. Understanding the spread of resistance is predicated on the surveillance of antibiotic resistance genes within an environment. Bioinformatics and artificial intelligence (AI) methods applied to metagenomic sequencing data offer the capacity to detect known and infer yet-unknown resistance mechanisms, and predict future outbreaks of antibiotic-resistant infections. Machine learning methods, in particular, could revive the waning antibiotic discovery pipeline by helping to predict the molecular structure and function of antibiotic resistance compounds, and optimising their interactions with target proteins. Consequently, AI has the capacity to play a central role in guiding antibiotic stewardship and future clinical decision-making around antibiotic resistance.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Bacterias / Inteligencia Artificial Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Curr Opin Microbiol Asunto de la revista: MICROBIOLOGIA Año: 2023 Tipo del documento: Article País de afiliación: Nueva Zelanda

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Bacterias / Inteligencia Artificial Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Curr Opin Microbiol Asunto de la revista: MICROBIOLOGIA Año: 2023 Tipo del documento: Article País de afiliación: Nueva Zelanda