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Verfasst von:López-Cortés, Andrés [VerfasserIn]   i
 Guevara-Ramírez, Patricia [VerfasserIn]   i
 Kyriakidis, Nikolaos C. [VerfasserIn]   i
 Barba-Ostria, Carlos [VerfasserIn]   i
 León Cáceres, Ángela [VerfasserIn]   i
 Guerrero, Santiago [VerfasserIn]   i
 Ortiz-Prado, Esteban [VerfasserIn]   i
 Munteanu, Cristian R. [VerfasserIn]   i
 Tejera, Eduardo [VerfasserIn]   i
 Cevallos-Robalino, Doménica [VerfasserIn]   i
 Gómez-Jaramillo, Ana María [VerfasserIn]   i
 Simbaña-Rivera, Katherine [VerfasserIn]   i
 Granizo-Martínez, Adriana [VerfasserIn]   i
 Pérez-M, Gabriela [VerfasserIn]   i
 Moreno, Silvana [VerfasserIn]   i
 García-Cárdenas, Jennyfer M. [VerfasserIn]   i
 Zambrano, Ana Karina [VerfasserIn]   i
 Pérez-Castillo, Yunierkis [VerfasserIn]   i
 Cabrera-Andrade, Alejandro [VerfasserIn]   i
 Puig San Andrés, Lourdes [VerfasserIn]   i
 Proaño-Castro, Carolina [VerfasserIn]   i
 Bautista, Jhommara [VerfasserIn]   i
 Quevedo, Andreina [VerfasserIn]   i
 Varela, Nelson [VerfasserIn]   i
 Quiñones, Luis Abel [VerfasserIn]   i
 Paz-y-Miño, César [VerfasserIn]   i
Titel:In silico analyses of immune system protein interactome network, single-cell RNA sequencing of human tissues, and artificial neural networks reveal potential therapeutic targets for drug repurposing against COVID-19
Verf.angabe:Andrés López-Cortés, Patricia Guevara-Ramírez, Nikolaos C. Kyriakidis, Carlos Barba-Ostria, Ángela León Cáceres, Santiago Guerrero, Esteban Ortiz-Prado, Cristian R. Munteanu, Eduardo Tejera, Doménica Cevallos-Robalino, Ana María Gómez-Jaramillo, Katherine Simbaña-Rivera, Adriana Granizo-Martínez, Gabriela Pérez-M, Silvana Moreno, Jennyfer M. García-Cárdenas, Ana Karina Zambrano, Yunierkis Pérez-Castillo, Alejandro Cabrera-Andrade, Lourdes Puig San Andrés, Carolina Proaño-Castro, Jhommara Bautista, Andreina Quevedo, Nelson Varela, Luis Abel Quiñones and César Paz-y-Miño
E-Jahr:2021
Jahr:26 February 2021
Umfang:24 S.
Teil:volume:12
 year:2021
 elocationid:598925
 pages:1-24
 extent:24
Fussnoten:Gesehen am 11.05.2021
Titel Quelle:Enthalten in: Frontiers in pharmacology
Ort Quelle:Lausanne : Frontiers Media, 2010
Jahr Quelle:2021
Band/Heft Quelle:12(2021), Artikel-ID 598925, Seite 1-24
ISSN Quelle:1663-9812
Abstract:Background: There is pressing urgency to identify therapeutic targets and drugs that allow treating COVID-19 patients effectively. Methods: We performed in silico analyses of immune system protein interactome network, single-cell RNA sequencing of human tissues, and artificial neural networks to reveal potential therapeutic targets for drug repurposing against COVID-19. Results: We screened 1,584 high-confidence immune system proteins in ACE2 and TMPRSS2 co-expressing cells, finding 25 potential therapeutic targets significantly overexpressed in nasal goblet secretory cells, lung type II pneumocytes, and ileal absorptive enterocytes of patients with several immunopathologies. Then, we performed fully connected deep neural networks to find the best multitask classification model to predict the activity of 10,672 drugs, obtaining several approved drugs, compounds under investigation, and experimental compounds with the highest area under the receiver operating characteristics. Conclusion: After being effectively analyzed in clinical trials, these drugs can be considered for treatment of COVID-19 patients. Scripts can be downloaded at https://github.com/muntisa/immuno-drug-repurposing-COVID-19.
DOI:doi:10.3389/fphar.2021.598925
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext ; Verlag: https://doi.org/10.3389/fphar.2021.598925
 Volltext: https://www.frontiersin.org/articles/10.3389/fphar.2021.598925/full
 DOI: https://doi.org/10.3389/fphar.2021.598925
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:artificial neural networks
 COVID-19
 drugs
 Immune System
 single-cell RNA sequencing
K10plus-PPN:1757668950
Verknüpfungen:→ Zeitschrift

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