Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach
Table 2
Model performance using several supervised machine-learning algorithms.
Various machine-learning algorithms were evaluated using the entire feature set (n = 3065 genes) and the Clairvoyance-optimized feature set (GeneSety1-y5, n = 399 genes) with the same LCOCV pairs. Performance metrics for each LCOCV set include accuracy, precision, recall, and F1 score. LCOCV refers to Leave Compound Out Cross Validation where we remove all instances of a compound from the data used to fit the model (training data) and evaluate performance on the held-out compound profiles (testing data) (see Materials and Methods).