Exploration of machine learning algorithms for predicting the changes in abundance of antibiotic resistance genes in anaerobic digestion.

Haffiez, Nervana; Chung, Tae Hyun; Zakaria, Basem S; Shahidi, Manjila; Mezbahuddin, Symon; Maal-Bared, Rasha; Dhar, Bipro Ranjan

Haffiez, Nervana; Chung, Tae Hyun; Zakaria, Basem S; Shahidi, Manjila; Mezbahuddin, Symon; Maal-Bared, Rasha; Dhar, Bipro Ranjan.

Afiliação

Haffiez N; Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada.
Chung TH; Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada.
Zakaria BS; Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada.
Shahidi M; 4S Analytics & Modelling Ltd., Edmonton, AB T6W 3V6, Canada.
Mezbahuddin S; 4S Analytics & Modelling Ltd., Edmonton, AB T6W 3V6, Canada.
Maal-Bared R; EPCOR Water Utilities, Edmonton, AB T5H 0E8, Canada.
Dhar BR; Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada. Electronic address: bipro@ualberta.ca.

Sci Total Environ ; 839: 156211, 2022 Sep 15.

Article em En | MEDLINE | ID: mdl-35623518

Assuntos

Antibacterianos; Eliminação de Resíduos; Algoritmos; Anaerobiose; Antibacterianos/farmacologia; Resistência Microbiana a Medicamentos/genética; Ecossistema; Alimentos; Genes Bacterianos; Aprendizado de Máquina; Esgotos

Palavras-chave

Anaerobic digestion; Antibiotic resistance genes; Machine learning; Mobile genetic elements

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Eliminação de Resíduos / Antibacterianos Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sci Total Environ Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Canadá

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