Abstract
The intestinal microbiota compositions of 92 Japanese men were identified following consumption of identical meals for 3 days, and collected feces were analyzed through terminal restriction fragment length polymorphism. The obtained operational taxonomic units (OTUs) and subjects' smoking and drinking habits, which had 2 nominal partitions, yes or no, were analyzed by Data mining software. Identification of subjects for each habit was successfully performed and reported previously, but the identification accuracy was closely dependent on the species of the applied restriction enzymes for PCR. For the sake of better selection of enzymes and understanding the mechanisms of Data mining analysis, 516f-BslI and 516f-HaeIII, 27f-MspI and 27f-AluI and 35f-HhaI, 35f-MspI and 35f-AluI, altogether 7 enzymes, were examined comparatively. Data mining analysis provides a Decision tree for identification of subjects and their dividing pathways that is produced using a limited number of OTUs, which affects the accuracy of the results. The present report discusses not only a global comparison of accuracies for characteristics, but also the detailed mechanisms that result in better or worse results and the practical roles and functions of OTUs. The OTU at the 1st step of the constructed Decision tree was the most important for any identification, and for all cases, the combination of subsequent OTUs, which formed later in the Decision tree, was also unignorable. Detailed dividing pathways were traced and compared for the 7 enzymes and the future supporting ideas were provided for better Data mining analysis of the human intestinal microbiota.