Figure 4

Performance analysis of the identified module biomarker. (A) The robustness of classification accuracy in perturbation data with different ratio of artificial noises. The mean accuracy of the proposed classifier decreases progressively from 84.02% to 73.26% when ratio of noise increases from 1% to 10%. (B) Comparison of biomarkers identified by different methods in GSE18732. ROC curves shows a superior performance in classification of module biomarker identified in this work (AUC = 0.96). (C) Histogram of mean accuracy with variance for biomarkers identified by our method, SVM-RFE and PAC. We also randomized the interactions of background network (PPIs) 50 times and identified a module biomarker using the proposed method, then mean accuracy and variance are calculated for 10-fold cross-validation across 5 datasets used in this work. Results show a stable performance across tissues for identified biomarkers.