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Identifying key genes related to inflammasome in severe COVID-19 patients based on a joint model with random forest and artificial neural network.
Ou, Haiya; Fan, Yaohua; Guo, Xiaoxuan; Lao, Zizhao; Zhu, Meiling; Li, Geng; Zhao, Lijun.
Affiliation
  • Ou H; Department of Gastroenterology, Shenzhen Bao'an Traditional Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen, China.
  • Fan Y; Traditional Chinese Medicine Innovation Research Center, Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Shenzhen, China.
  • Guo X; Laboratory Animal Center, Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Lao Z; Traditional Chinese Medicine Innovation Research Center, Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Shenzhen, China.
  • Zhu M; Laboratory Animal Center, Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Li G; Traditional Chinese Medicine Innovation Research Center, Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Shenzhen, China.
  • Zhao L; Laboratory Animal Center, Guangzhou University of Chinese Medicine, Guangzhou, China.
Front Cell Infect Microbiol ; 13: 1139998, 2023.
Article in En | MEDLINE | ID: mdl-37113134
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Inflammasomes / COVID-19 Type of study: Clinical_trials / Systematic_reviews Limits: Humans Language: En Journal: Front Cell Infect Microbiol Year: 2023 Document type: Article Affiliation country: China Country of publication: Switzerland
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Inflammasomes / COVID-19 Type of study: Clinical_trials / Systematic_reviews Limits: Humans Language: En Journal: Front Cell Infect Microbiol Year: 2023 Document type: Article Affiliation country: China Country of publication: Switzerland