Notably, both models outperformed GBM algorithms trained using only MGI values from one of the classic markers of reactive astrocytes (GFAP: accuracy 67.25%, AUC = 0.7154) or microglia (MHC2: accuracy 61.93%, AUC = 0.6563; CD68: accuracy 63.61%, AUC = 0.6831), demonstrating that a combination of reactive and homeostatic markers adds predictive value for the CTRL vs. AD classification. The gene discussed is GFAP; the disease is Alzheimer disease.