Fig. 3: ML performance and feature selection from correlations between gut microbial species, resistome and antibiotic resistance in E. coli.

a, Performance of the ML-powered predictive functions of E. coli resistance to specific antibiotics (ML technology: extra tree classifier; see Methods). Performance indicators (AUC, accuracy and precision) were computed as the average of 30 iterations of nested cross-validation (see Methods). See Supplementary Fig. 2 for performance indicator sensitivity, specificity and Cohen’s kappa score. The violin plots show the distribution of the data, with each data point representing one antibiotic model. Inside each violin plot is a box plot, with the box showing the interquartile range (IQR), the whiskers showing the rest of the distribution as a proportion of 1.5 x IQR and the white circle representing the median value. b, Counts of metagenomic features (ARGs and microbial species) found as the strongest predictors of E. coli resistance/susceptibility profiles to each antibiotic. c, Undirected graph showing the strongest predictors (metagenomic features in the chicken gut) for each antibiotic model. The edges of the graph link ARG or bacteria species nodes (predictor variables) to the antibiotic model in which they were found to be predictive. Both the ARG and antibiotic model nodes are colour coded according to the antibiotic class that the antibiotic/ARG is known to be associated with. The ML models were run for the following antibiotics: amoxicillin–clavulanic acid (AMC), amikacin (AMI), aztreonam (AZM), ceftazidime (CAZ), ceftazidime–clavulanic acid (CAZ-C), cefotaxime (CTX), cefotaxime–clavulanic acid (CTX-C), cefoxitin (CFX), chloramphenicol (CHL), cefepime (FEP), gentamycin (GEN), kanamycin (KAN), minocycline (MIN), nalidixic acid (NAL), streptomycin (STR), sulfafurazole (SUL) and trimethoprim–sulfamethoxazole (SXT). MDR, multidrug resistant.