Reliability of the In Silico Prediction Approach to In Vitro Evaluation of Bacterial Toxicity.

Ahn, Sung-Yoon; Kim, Mira; Bae, Ji-Eun; Bang, Iel-Soo; Lee, Sang-Woong

Ahn, Sung-Yoon; Kim, Mira; Bae, Ji-Eun; Bang, Iel-Soo; Lee, Sang-Woong.

Afiliação

Ahn SY; Pattern Recognition and Machine Learning Lab, Department of AI Software, Gachon University, Seongnam 13557, Korea.
Kim M; Department of Microbiology and Immunology, Chosun University School of Dentistry, Gwangju 61452, Korea.
Bae JE; Department of Microbiology and Immunology, Chosun University School of Dentistry, Gwangju 61452, Korea.
Bang IS; Department of Microbiology and Immunology, Chosun University School of Dentistry, Gwangju 61452, Korea.
Lee SW; Pattern Recognition and Machine Learning Lab, Department of AI Software, Gachon University, Seongnam 13557, Korea.

Sensors (Basel) ; 22(17)2022 Aug 31.

Article em En | MEDLINE | ID: mdl-36081016

Assuntos

Poluição do Ar em Ambientes Fechados; COVID-19; Microbiologia do Ar; Poluição do Ar em Ambientes Fechados/análise; Bactérias; Fungos; Humanos; Pandemias; Reprodutibilidade dos Testes

Palavras-chave

BERT; protein; toxin; virulence factors

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Poluição do Ar em Ambientes Fechados / COVID-19 Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Sensors (Basel) Ano de publicação: 2022 Tipo de documento: Article

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