| Summary: | Antibiotic pollution in the environment can significantly impact soil microorganisms, such as altering the soil microbial community or emerging antibiotic-resistant bacteria. We propose three machine learning (ML) methods to investigate antibiotics' impact on microorganisms and predict microbial abundance. We examined the microbial abundances of various environmental soil samples treated with antibiotics. We developed 3 ML models: (Model 1) for predicting the most abundant bacterial classes in a specific treatment group; (Model 2) for predicting antibiotic treatment effects based on bacterial abundances; and (Model 3) for using data from short-term incubations to predict the data of community structure after stabilisation. In Model 1, the Random Forest model achieved the highest average accuracy, with a Coefficient of Variation mean of 0.05 and 0.14 in the training and test set. In Model 2, the accuracy of the random forest and SVM models have the highest accuracy (nearly 0.90). Model 3 demonstrates that the Random Forest can use data from short-term incubations to predict the abundance of bacterial communities after long-term stabilisation. This study highlights the potential of ML models as powerful tools for understanding microbial dynamics in response to antibiotic treatments. The code is publicly available at - https://github.com/DeweyYihengDu/ML_on_Microbiota.
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