Extended Data Fig. 1: Performance of different metrics and models in capturing XR and CS antibiotic interactions from chemical genetics data.

a, Receiver operating characteristic (ROC) curves for classification of XR (positive class) vs non-XR (negative class), and CS (positive class) vs non-CS (negative class), using simple linear and non-linear correlation metrics. AUC is the area under the curve. b, The performance of the decision tree model on balanced classes shows that both XR and CS interactions can be well classified. c, Decision tree with classes CS (class 1) versus the rest (class 0), where a maximum depth of 3 is shown for visualization, illustrates the hierarchy of decisions to discriminate classes. Each node in the tree represents a decision point based on the value of a particular feature, and branches represent the outcome of the decision. The root node divides the data based on the ‘concordant_negative_w’ feature, which is the sum of s-scores (as weights) of hits on the negative concordant site of a scatterplot. The tree branches out to ‘discordant_w’ feature, which is the sum of s-scores (as weights) of hits on the discordant site of a scatterplot, while ‘discordant_w_m’ is the sum of products of s-scores (as weights) of hits on the discordant site of a scatterplot. d, P values from a paired Mann–Whitney U-test (two-sided) are depicted across quantile cutoffs for extreme s-scores to differentiate XR/CS/neutral interactions based on OCDM values. Q3 and Q97 perform the best. e, Confusion matrix of results based on Q3 and Q97. Most interactions inferred as non-XR/non-CS were previously reported neutral. For more information, see also Extended Data Fig. 2a-c.

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