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.

Afiliación

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 en En | MEDLINE | ID: mdl-36920635

Asunto(s)

Atenolol; Sepsis; Humanos; Teorema de Bayes; Simulación del Acoplamiento Molecular; Sepsis/diagnóstico; Aprendizaje Automático

Palabras clave

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

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Atenolol / Sepsis Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Inflammation Año: 2023 Tipo del documento: Article Pais de publicación: Estados Unidos

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