Machine learning framework develops neutrophil extracellular traps model for clinical outcome and immunotherapy response in lung adenocarcinoma - Han, A. Xuan; Long, B. Yaping; Li, C. Yao; Huang, D. Di; Xiong, E. Qi; Li, F. Jinfeng; Wu, G. Liangliang; Liu, Qiaowei; Yang, G. Bo; Hu, H. Yi (Apoptosis (London), 2024)
Keywords: Adenocarcinoma; Adenocarcinoma of Lung - genetics; Adenocarcinoma of Lung - immunology; Adenocarcinoma of Lung - pathology; Algorithms; Animals; Biochemistry; Biomedical and Life Sciences; Biomedicine; Cancer; Cancer Research; Cell Biology; Cell death; Cell Line, Tumor; Cluster analysis; Clusters; DNA microarrays; Extracellular Traps - immunology; Extracellular Traps - metabolism; Gene Expression Regulation, Neoplastic; Genes; Humans; Immunotherapy; In vivo methods and tests; Learning algorithms; Leukocytes (neutrophilic); Lung cancer; Lung Neoplasms - genetics; Lung Neoplasms - immunology; Lung Neoplasms - pathology; Lungs; Machine Learning; Medical prognosis; Metastases; Mice; Microenvironments; Neutrophils; Neutrophils - immunology; Neutrophils - metabolism; Oncology; Performance prediction; Phenotypes; Phospholipid Transfer Proteins - genetics; Phospholipid Transfer Proteins - metabolism; Phospholipids; Prognosis; Therapeutic targets; Tumor Microenvironment - immunology; Tumors; Virology; Prognostic model; PLSCR1; Lung adenocarcinoma; Cluster immunophenotype; Neutrophil extracellular traps; Machine learning