Molecular subtypes predict therapeutic responses and identifying and validating diagnostic signatures based on machine learning in chronic myeloid leukemia - Zhong, Fang-Min; Yao, Fang-Yi; Yang, Yu-Lin; Liu, Jing; Li, Mei-Yong; Jiang, Jun-Yao; Zhang, Nan; Xu, Yan-Mei; Li, Shu-Qi; Cheng, Ying; Xu, Shuai; Huang, Bo; et al. (Cancer cell international, 2023)
Keywords: Algorithms; Antigen presentation; Antigen processing; Aquaporin 3; Biomarkers; Biomedical and Life Sciences; Biomedicine; Cancer Research; CD4 antigen; CD8 antigen; Cell activation; Cell Biology; Cell differentiation; Chronic myeloid leukemia; CTLA-4 protein; Diagnosis; Differential diagnosis; Gene expression; Glycolysis; Immunodeficiency; Immunosuppression; Inflammation; Inhibitor drugs; Kinases; Leukemia; Lymphocytes T; Machine learning; Malignancy; Metastases; MicroRNAs; Myeloid leukemia; Pathogenesis; Patients; Phenotypes; Reactive oxygen species; Regression analysis; Signal transduction; Software; Support vector machines; T cell receptors; Therapeutic response; Tumor microenvironment; Variables; Therapeutic response; Chronic myeloid leukemia; Diagnosis; Tumor microenvironment; Machine learning