Machine learning-based techniques, including LASSO, Boruta, Support Vector Machine, and Random Forest, were employed to specifically identify CTSC, TGFBI, and GMFG as potential diagnostic biomarkers associated with SMC activity in atherosclerosis. The gene discussed is TGFBI; the disease is atherosclerosis.