Predicting oral malodour based on the microbiota in saliva samples using a deep learning approach.

ABSTRACT:

Background

Oral malodour is mainly caused by volatile sulphur compounds produced by bacteria and bacterial interactions. It is difficult to predict the presence or absence of oral malodour based on the abundances of specific species and their combinations. This paper presents an effective way of deep learning approach to predicting the oral malodour from salivary microbiota.

Methods

The 16S rRNA genes from saliva samples of 90 subjects (45 had no or weak oral malodour, and 45 had marked oral malodour) were amplified, and gene sequence analysis was carried out. Deep learning classified oral malodour and healthy breath based on the resultant abundances of operational taxonomic units (OTUs) RESULTS: A discrimination classifier model was constructed by profiling OTUs and calcula

SUBMITTER: Nakano Y 

PROVIDER: S-EPMC6069980 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature