Supervised learning: Classification | Fig 3A illustrates the predictions on one replicate (combining all five of its folds, with each serving separately as test data) and Fig 3B summarizes the resulting area-under-ROC-curve (AUC) over all 200 replicates (computing AUC only on test data). |
Supervised learning: Classification | Nonetheless, even with a rigorous 200-replicate fivefold cross-validation, a mean AUC of 0.83 (standard deviation of 0.10) was observed, indicating that antibody features are highly and robustly predictive of high vs. low ADCP activity. |
Supervised learning: Classification | Despite the reduction in data considered, Fig 3C and 3D shows that the resulting performance with the filtered feature set is comparable to that with the complete feature set, with a mean AUC of 0.84 (standard deviation 0.10). |
readily interpretable. | (AF) Prediction results by ZOO-replicate fivefold cross-validation, illustrating PLR values (>0.5 predicted high ADCP; <O.5 predicted low) for one replicate (A,C,E) and providing area under the ROC curve ( AUC ) over all 200 replicates (B,D,F). |
readily interpretable. | (AF) Prediction results by ZOO-replicate fivefold cross-validation, illustrating PLR values (>0.5 predicted high ADCP; <O.5 predicted low) for one replicate (A,C,E) and providing area under the ROC curve ( AUC ) over all 200 replicates (B,D,F). |
Application to pathogen infection experiments | Both networks show high AUC values and even better accuracy (see Table 1). |
Supporting Information | In (A) the area under the ROC curve ( AUC ) was calculated based on edge frequencies of the samples. |
Supporting Information | The second and third column show performance of the networks in terms of accuracy (ACC) and area under curve ( AUC ). |