Leveraging machine learning for prediction of antibiotic resistance genes post thermal hydrolysis-anaerobic digestion in dairy waste

Abstract

Anaerobic digestion holds promise as a method for removing antibiotic resistance genes (ARGs) from dairy waste. However, accurately predicting the efficiency of ARG removal remains a challenge. This study introduces a novel appproach utilizing machine learning to forecast changes in ARG abundances following thermal hydrolysis-anaerobic digestion (TH-AD) treatment. Through network analysis and redundancy analyses, key determinants of affect ARG fluctuations were identified, facilitating the development of machine learning models capable of accurately predicting ARG changes during TH-AD processes. The decision tree model demonstrated impressive predictive power, achieving an impessive R2 value of 87% against validation data. Feature analysis revealed that the genes intI2 and intI1 had a critical impact on the absolute abundance of ARGs. The predictive model developed in this study offers valuable insights for improving operational and managerial practices in dairy waste treatment facilities, with the ultimate goal of mitigating the spread of antibiotic resistance.