Single-cell Sequence Analysis Combined with Multiple Machine Learning to Identify Markers in Sepsis Patients: LILRA5.

Ning, Jingyuan; Fan, Xiaoqing; Sun, Keran; Wang, Xuan; Li, Hongru; Jia, Keqi; Ma, Cuiqing

Ning, Jingyuan; Fan, Xiaoqing; Sun, Keran; Wang, Xuan; Li, Hongru; Jia, Keqi; Ma, Cuiqing.

Afiliação

Ning J; Department of Immunology, Immunology Department of Hebei Medical University, Shijiazhuang, People's Republic of China.
Fan X; Department of Immunology, Immunology Department of Hebei Medical University, Shijiazhuang, People's Republic of China.
Sun K; Department of Immunology, Immunology Department of Hebei Medical University, Shijiazhuang, People's Republic of China.
Wang X; Department of Immunology, Immunology Department of Hebei Medical University, Shijiazhuang, People's Republic of China.
Li H; Department of Laboratory, The Second Hospital of Hebei Medical University, Shijiazhuang, People's Republic of China.
Jia K; Department of Immunology, Immunology Department of Hebei Medical University, Shijiazhuang, People's Republic of China.
Ma C; Department of Pathology, Pathology Department of Shijiazhuang People's Hospital, Shijiazhuang, People's Republic of China.

Inflammation ; 46(4): 1236-1254, 2023 Aug.

Article em En | MEDLINE | ID: mdl-36920635

Assuntos

Atenolol; Sepse; Humanos; Teorema de Bayes; Simulação de Acoplamento Molecular; Sepse/diagnóstico; Aprendizado de Máquina

Palavras-chave

LILRA5; machine learning; molecular docking; sepsis; single cell

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Atenolol / Sepse Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: Inflammation Ano de publicação: 2023 Tipo de documento: Article

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