Integrative transcriptomic, proteomic, and machine learning approach to identifying feature genes of atrial fibrillation using atrial samples from patients with valvular heart disease - Liu, Yaozhong; Bai, Fan; Tang, Zhenwei; Liu, Na; Liu, Qiming (BMC cardiovascular disorders, 2021)
Keywords: Algorithms; Angiology; Arrhythmia; Arrhythmias and Electrophysiology; Atrial fibrillation; Bayesian analysis; Biomarkers; Blood Transfusion Medicine; Cardiac arrhythmia; Cardiac Surgery; Cardiology; Cardiovascular disease; Cardiovascular research; CD44 antigen; Coronary artery disease; Datasets; Diagnosis; DNA microarrays; Feature gene; Feature selection; FHL2 protein; Fibrillation; Gene expression; Genetic aspects; Genomes; Heart diseases; Heart surgery; Heart valve diseases; Insulin-like growth factor-binding protein 2; Internal Medicine; Learning algorithms; Machine learning; Medicine; Medicine & Public Health; Meta-analysis; Methods; Proteomic; Proteomics; Research Article; Sample size; Scientific imaging; Software; Surgery; Therapeutic targets; Transcriptomic; China; Transcriptomic; Atrial fibrillation; Proteomic; Machine learning; Feature gene