Fig. 3

Disease status prediction based on response genes. a, b Risk score distribution for the population test (a) and the clinic test (b) predicted from the clinical variables and the expression of the response genes using a penalized regression (see the ‘Methods’ section). The risk distributions are presented separately for healthy volunteers (green), clinic patients without cancer (orange) and clinic patients with cancer (purple). c, d ROC curves for the population (c) and clinic (d) scores. For each case, we present the ROC curve for the model trained on clinical data (triangles) or on gene expression and clinical data (squares). Each curve is an average obtained across 100 cross-validation (CV) experiments and the grey area surrounding the curve gives the standard error. The color of the curve represents the test threshold corresponding to the represented sensitivity/false-positive rate compromise. (Inset) Area under the ROC curve, in 100 CV rounds, for a clinical-only model (red) the model constructed on the response genes (blue) and a model constructed on a combination of clinical information and response genes (green) for the population (c) and clinic (d) classifiers. P values given above each box are computed using a 2-sample t-test. e The population and clinic classifiers applied to nasal samples from the AEGIS cohort