New workflow predicts drug targets against SARS-CoV-2 via metabolic changes in infected cells.

Leonidou, Nantia; Renz, Alina; Mostolizadeh, Reihaneh; Dräger, Andreas

Leonidou, Nantia; Renz, Alina; Mostolizadeh, Reihaneh; Dräger, Andreas.

Afiliação

Leonidou N; Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karls University of Tübingen, Tübingen, Germany.
Renz A; Department of Computer Science, Eberhard Karls University of Tübingen, Tübingen, Germany.
Mostolizadeh R; Cluster of Excellence 'Controlling Microbes to Fight Infections', Eberhard Karls University of Tübingen, Tübingen, Germany.
Dräger A; Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karls University of Tübingen, Tübingen, Germany.

PLoS Comput Biol ; 19(3): e1010903, 2023 03.

Article em En | MEDLINE | ID: mdl-36952396

Assuntos

COVID-19; SARS-CoV-2; Humanos; SARS-CoV-2/genética; Fluxo de Trabalho; Antivirais/farmacologia; Antivirais/uso terapêutico; Células Epiteliais

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: SARS-CoV-2 / COVID-19 Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: PLoS Comput Biol Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Alemanha

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