FIGURE 5.

Selection of an antigen/antibody isotype signature using machine learning algorithms applied to the controlled human infection typhoid datasets. (A) Test set prediction performance measures AUC receiver operator characteristic (ROC) and balanced accuracies (BalAcc) for four different machine learning models using 500 bootstrap samples of the data. (B) Frequency of features selected in each of 500 iterations by the partial least squares (PLS) algorithm. (C) Proportions of features selected across all 500 bootstrap samples using the PLS algorithm. Features had to be selected in at least 10% of the bootstrap samples (column ‘overall’). Proportions are split by classifier size. The last column represents the overall proportion across all 500 bootstrap samples.