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Random Forest classification model was applied on the 16S rRNA abundance data to identify most important microbial features in TT, NT, and saliva samples. Features with at least 4 reads and with a minimum prevalence of 10% across samples were included. Data was further transformed to centered log ratio (CLR) before applying the Random Forest classification algorithm
