Supervised method for periodontitis phenotypes prediction based on microbial composition using 16S rRNA sequences.

Abstract

Microbes play an important role on human health, however, little is known on microbes in the past decades for the limitation of culture-based techniques. Recently, with the development of next-generation sequencing (NGS) technologies, it is now possible to sequence millions of sequences directly from environments samples, and thus it supplies us a sight to probe the hidden world of microbial communities and detect the associations between microbes and diseases. In the present work, we proposed a supervised learning-based method to mine the relationship between microbes and periodontitis with 16S rRNA sequences. The jackknife accuracy is 94.83% and it indicated the method can effectively predict disease status. These findings not only expand our understanding of the association between microbes and diseases but also provide a potential approach for disease diagnosis and forensics.