Skip to main content

Advertisement

Fig. 3 | Clinical Epigenetics

Fig. 3

From: Machine learning selected smoking-associated DNA methylation signatures that predict HIV prognosis and mortality

Fig. 3

a Importance rank in 997 CpG sites using GLMNET method following 100 bootstraps. Only top-ranked 20 CpG sites are displayed. b Performance of the selected features predicting high HIV frailty (Veteran Aging Cohort index, VACS index) in a test sample set measured by area under curve (AUC) in receiver operating characteristic analysis. Ensemble-based machine learning from GLMNET, RF, SVM, and XGBoost was applied. c Performance of the selected features predicting high HIV frailty (Veteran Aging Cohort index, VACS index) in a test sample set measured by balanced accuracy. Ensemble-based machine learning from four base machine learning methods, GLMNET, RF, SVM, and XGBoost, was applied. d Venn plot showing the overlapping CpG sites between the selected 698 features and epigenome-wide significant CpG sites for tobacco smoking

Back to article page