Due to the close interaction between the host and the gut microbiota, the alterations in gut microbiota metabolism may therefore contribute to various diseases. How to use antibiotics more wisely in clinical practice is a promising task in the field of pathophysiology related to gut microbiota. The hope fueling this research is that the alteration of gut microbial communities are paralleled by their capacity on metabolomic from the combined perspective of microbiome and metabolomics. In order to reveal the impacts of antibiotics on microbiota-associated host metabolomic phenotypes, a feasible methodology should be well developed to assess the pervasive effects of antibiotics on the population structure of gut microbial communities. Our attempt starts from predicting specific resistance phenotypes of the individuals in isolation from the rest of the gut microbiota community, according to their resistant genotypes. Once resistance phenotypes of microbiome is determined, we integrated metabolomics with machine learning by applying various analysis algorithms to explore the relationship between the predicted resistance and metabolites, including what the microbial community is after medication, which microbes produce metabolites, and how these metabolites enrich.
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