Title: Supervised method for periodontitis phenotypes prediction based on microbial composition using 16S rRNA sequences
Authors: Wei Chen; Yong-Mei Cheng; Shao-Wu Zhang; Quan Pan
Addresses: College of Automation, Northwestern Polytechnical University, Xi'an, 710072, China; Department of Biostatistics, Yale University, New Haven, CT 06510, USA ' College of Automation, Northwestern Polytechnical University, Xi'an, 710072, China ' College of Automation, Northwestern Polytechnical University, Xi'an, 710072, China ' College of Automation, Northwestern Polytechnical University, Xi'an, 710072, China
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.
Keywords: microbial communities; 16S rRNA genes; OTU; operational taxonomic unit; periodontitis phenotype; supervised learning; human-association diseases; elastic net; microbial composition; 16S rRNA sequences; microbes; next-generation sequencing; NGS; disease diagnosis; forensics; gene sequences.
International Journal of Computational Biology and Drug Design, 2014 Vol.7 No.2/3, pp.214 - 224
Published online: 27 May 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article