Feasibility study of machine learning to explore relationships between antimicrobial resistance and microbial...

Wastewater sludges (WSs) are major reservoirs and emission sources of antibiotic resistance genes (ARGs) in cities. Identifying antimicrobial resistance (AMR) host bacteria in WSs is crucial for understanding AMR formation and mitigating biological and ecological risks. Here 24 sludge data from wastewater treatment plants in Jiangsu Province, China, and 1559 sludge data from genetic databases were analyzed to explore the relationship between 7 AMRs and bacterial distribution. The results of the Procrustes and Spearman correlation analysis were unsatisfactory, with p-value exceeding the threshold of 0.05 and no strong correlation (r > 0.8). In contrast, explainable machine learning (EML) using SHapley Additive exPlanation (SHAP) revealed Pseudomonadota as a major contributor (39.3 %-74.2 %) to sludge AMR. Overall, the application of ML is promising in analyzing AMR-bacteria relationships. Given the different applicable occasions and advantages of various analysis methods, using ML as one of the correlation analysis tools is strongly recommended.