TABLE 1.

Commonly used ML algorithms in AMR investigations^a

Algorithm	Learning method	Feature evaluation	Traceable	Interaction	AMR investigation
Logistic regression	Regression algorithm with logistic curve that associates weights to each input features (134)	Yes	No	Nob	Maguire et al. (30), investigated primary AMR drivers of nontyphoidal Salmonella with known AMR determinants as features
Support vector machines	Separates labeled training data via constructing an optimal hyperplane, grouping appropriate genes, k-mer, or SNV features together (135)	Noc	No	Yes	Niehaus et al. (136) used M. tuberculosis SNVs to develop resistance prediction models for the 4 common first-line drugs
Random forest	Set of decision trees with internal nodes containing a series of questions about relevant features; different answers are directed to separate child nodes until reaching the final class label (86)	Yes	Yes	Yes	Moradigaravand et al. (88) predicted AMR from E. coli pangenome with various feature representations including the presence-absence of accessory genomes and the population structure inferred from the core genome
Rule based	Set of IF-THEN statements with condition and a prediction	Yes	Yes	Yes	Drouin et al. (73) predicted resistance of Gram-negative and -positive pathogens using k-mer features and the optimized set covering machine
Neural networks	Models loosely inspired by the structure of human brains, including deep learning (DL) models, and capable of modeling complex nonlinear relationships but require large amounts of data	Yes	No	Yes	Yang et al. (29) used M. tuberculosis SNV with DL to predict resistance to the four first-line antituberculosis drugs

^aFeature evaluation is when the algorithm can weigh or rank the features’ impact on the prediction. A traceable algorithm allows visualization of the logical flow that leads to a prediction. Interaction indicates whether the algorithm can represent feature interdependencies.

^bWithout additional data processing.

^cUnless using a linear kernel.