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.

Afiliación

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 en En | MEDLINE | ID: mdl-36081016

Asunto(s)

Contaminación del Aire Interior; COVID-19; Microbiología del Aire; Contaminación del Aire Interior/análisis; Bacterias; Hongos; Humanos; Pandemias; Reproducibilidad de los Resultados

Palabras clave

BERT; protein; toxin; virulence factors

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Contaminación del Aire Interior / COVID-19 Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article Pais de publicación: Suiza

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