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Machine learning approaches for influenza A virus risk assessment identifies predictive correlates using ferret model in vivo data.
Kieran, Troy J; Sun, Xiangjie; Maines, Taronna R; Belser, Jessica A.
Afiliação
  • Kieran TJ; Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA. tkieran@cdc.gov.
  • Sun X; Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA.
  • Maines TR; Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA.
  • Belser JA; Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA. jbelser@cdc.gov.
Commun Biol ; 7(1): 927, 2024 Aug 01.
Article em En | MEDLINE | ID: mdl-39090358
ABSTRACT
In vivo assessments of influenza A virus (IAV) pathogenicity and transmissibility in ferrets represent a crucial component of many pandemic risk assessment rubrics, but few systematic efforts to identify which data from in vivo experimentation are most useful for predicting pathogenesis and transmission outcomes have been conducted. To this aim, we aggregated viral and molecular data from 125 contemporary IAV (H1, H2, H3, H5, H7, and H9 subtypes) evaluated in ferrets under a consistent protocol. Three overarching predictive classification outcomes (lethality, morbidity, transmissibility) were constructed using machine learning (ML) techniques, employing datasets emphasizing virological and clinical parameters from inoculated ferrets, limited to viral sequence-based information, or combining both data types. Among 11 different ML algorithms tested and assessed, gradient boosting machines and random forest algorithms yielded the highest performance, with models for lethality and transmission consistently better performing than models predicting morbidity. Comparisons of feature selection among models was performed, and highest performing models were validated with results from external risk assessment studies. Our findings show that ML algorithms can be used to summarize complex in vivo experimental work into succinct summaries that inform and enhance risk assessment criteria for pandemic preparedness that take in vivo data into account.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Vírus da Influenza A / Infecções por Orthomyxoviridae / Furões / Aprendizado de Máquina Limite: Animals Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Vírus da Influenza A / Infecções por Orthomyxoviridae / Furões / Aprendizado de Máquina Limite: Animals Idioma: En Ano de publicação: 2024 Tipo de documento: Article