Figure 1.

Test set receiver operating characteristic (ROC) curve and area under the curve (AUC) showing the performance of the optimized classifier model for distinguishing 201 cancer patients from 163 cancer-free individuals of the Cohen et al. dataset

DEcancer_P uses proteins-only and DEcancer_PDE includes all 39 proteins, DNA-based and epidemiology factors. (Blue) The DEcancer_PDE approach for all cancer versus cancer-free test set ROC curve showing performance of optimal model. An AUC of 1.00 is achieved. At a fixed specificity of 99%, DEcancer achieves a sensitivity of 99%. (Orange) The 28-protein model uses the DEcancer_P approach for all cancer versus cancer-free test set ROC curve. An AUC of 1.00 is achieved. At a fixed specificity of 99%, DEcancer achieves a sensitivity of 93%.