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Identification and Analysis of a Four-Gene Set for Diagnosing SFTS Virus Infection Based on Machine Learning Methods and Its Association with Immune Cell Infiltration.

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第一作者： Tao,Huang

第一单位： National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), Institute for Viral Disease Control and Prevention, China CDC, Beijing 102206, China.

作者： Tao,Huang ^[1] ; Xueqi,Wang ^[2] ; Yuqian,Mi ^[3] ; Tiezhu,Liu ^[1] ; Yang,Li ^[4] ; Ruixue,Zhang ^[1] ; Zhen,Qian ^[1] ; Yanhan,Wen ^[1] ; Boyang,Li ^[1] ; Lina,Sun ^[1] ; Wei,Wu ^[1] ; Jiandong,Li ^[1] ; Shiwen,Wang ^[1] ; Mifang,Liang ^[1]

作者单位： National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), Institute for Viral Disease Control and Prevention, China CDC, Beijing 102206, China. ^[1] Capital Institute of Pediatrics, Beijing 100020, China. ^[2] Shanxi Academy of Advanced Research and Innovation, Taiyuan 030032, China. ^[3] Chongqing Research Institute of Big Data, Peking University, Chongqing 400039, China. ^[4]

关键词 LASSO–Cox SFTS SFTS acute phase immune cells infiltration machine learning

医学主题词人类(Humans);布尼亚病毒科感染(Bunyaviridae Infections);白蛉病毒(Phlebovirus);环氮氧化物(Cyclic N-Oxides);乙基亚硝脲(Ethylnitrosourea)

DOI 10.3390/v15102126

PMID 37896902

发布时间 2023-11-03

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