Leveraging genetic interactions for adverse drug-drug interaction prediction
Fig 2
The train-test splitting scheme and model performance on the test set.
(a) The train-test splitting scheme. Drugs are randomly divided into “training drugs” and “test drugs” with ratio of 2:1. Training set only consists of drug pairs constituted by “training drugs” and test set only consists of drug pairs constituted by “test drugs”. Training drugs are further split into “training drugsi” and “validation drugsi” with the same splitting scheme to obtain training seti and validation seti in the training phase. For each iteration of hold-out validation, the classifier is fit with training seti and evaluated with validation seti. Purple squares represent non-interacting drug pairs in training seti. Blue squares represent non-interacting drug pairs in validation seti. Green squares represent non-interacting drug pairs in test set. Red squares represent interacting drug pairs in each set. Grey squares represent unused drug pairs. (b) Approximate receiver operating characteristic (ROC) curves on the training set. (c) Approximate precision-recall curves on the training set. (d) AUROCs and AUPRs on the training set and the test set. (e) Receiver operating characteristic (ROC) curve on the test set. (f) Precision-recall curve on the test set.