How to make more from exposure data? An integrated machine learning pipeline to predict pathogen exposure.

Fountain-Jones, Nicholas M; Machado, Gustavo; Carver, Scott; Packer, Craig; Recamonde-Mendoza, Mariana; Craft, Meggan E

Fountain-Jones, Nicholas M; Machado, Gustavo; Carver, Scott; Packer, Craig; Recamonde-Mendoza, Mariana; Craft, Meggan E.

Afiliação

Fountain-Jones NM; Department of Veterinary Population Medicine, University of Minnesota, Saint Paul, MN, USA.
Machado G; Department of Population Health and Pathobiology, North Carolina State University, Raleigh, NC, USA.
Carver S; Department of Biological Sciences, University of Tasmania, Hobart, Tas., Australia.
Packer C; Department of Ecology Evolution and Behavior, University of Minnesota, Saint Paul, MN, USA.
Recamonde-Mendoza M; Institute of Informatics, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
Craft ME; Department of Veterinary Population Medicine, University of Minnesota, Saint Paul, MN, USA.

J Anim Ecol ; 88(10): 1447-1461, 2019 10.

Article em En | MEDLINE | ID: mdl-31330063

Assuntos

Vírus da Cinomose Canina; Leões; Animais; Animais Selvagens; Ecologia; Aprendizado de Máquina

Palavras-chave

boosted regression trees; disease ecology; gradient boosting models; machine learning; model-agnostic methods; random forests; serology; support vector machines

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Vírus da Cinomose Canina / Leões Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Animals Idioma: En Ano de publicação: 2019 Tipo de documento: Article

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