ARHGEF18 and Sepsis: Fan et al. utilized machine learning algorithms (random forests, support vector machine, etc.)from multiple GEO datasets to identify 15 sepsis shock diagnostic gene markers (e.g., CLEC5A, DUSP3, ARHGEF18), where ARHGEF18 and FCER1A were significantly related to patient survival; the RF model’s diagnostic AUC reached 0.993, demonstrating excellent clinical potential [28].