Exploring the predictive capability of advanced machine learning in identifying severe disease phenotype in Salmonella enterica.

Karanth, Shraddha; Tanui, Collins K; Meng, Jianghong; Pradhan, Abani K

Karanth, Shraddha; Tanui, Collins K; Meng, Jianghong; Pradhan, Abani K.

Afiliação

Karanth S; Department of Nutrition and Food Science, University of Maryland, College Park, MD 20742, USA.
Tanui CK; Department of Nutrition and Food Science, University of Maryland, College Park, MD 20742, USA; Center for Food Safety and Security Systems, University of Maryland, College Park, MD 20742, USA.
Meng J; Department of Nutrition and Food Science, University of Maryland, College Park, MD 20742, USA; Center for Food Safety and Security Systems, University of Maryland, College Park, MD 20742, USA; Joint Institute for Food Safety and Applied Nutrition, University of Maryland, College Park, MD 20742, USA.
Pradhan AK; Department of Nutrition and Food Science, University of Maryland, College Park, MD 20742, USA; Center for Food Safety and Security Systems, University of Maryland, College Park, MD 20742, USA. Electronic address: akp@umd.edu.

Food Res Int ; 151: 110817, 2022 01.

Article em En | MEDLINE | ID: mdl-34980422

Assuntos

Salmonella enterica; Animais; Aprendizado de Máquina; Fenótipo; Salmonella/genética; Salmonella enterica/genética; Suínos; Sequenciamento Completo do Genoma

Palavras-chave

Disease phenotypes; Disease severity; Food safety; Machine learning; Predictive modeling; Salmonella

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Salmonella enterica Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Animals Idioma: En Revista: Food Res Int Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

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