Supervised method for periodontitis phenotypes prediction based on microbial composition using 16S rRNA sequences Online publication date: Tue, 21-Oct-2014
by Wei Chen; Yong-Mei Cheng; Shao-Wu Zhang; Quan Pan
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 7, No. 2/3, 2014
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.
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