A Dirichlet-Multinomial Bayes Classifier for Disease Diagnosis with Microbial Compositions.

ABSTRACT: Dysbiosis of microbial communities is associated with various human diseases, raising the possibility of using microbial compositions as biomarkers for disease diagnosis. We have developed a Bayes classifier by modeling microbial compositions with Dirichlet-multinomial distributions, which are widely used to model multicategorical count data with extra variation. The parameters of the Dirichlet-multinomial distributions are estimated from training microbiome data sets based on maximum likelihood. The posterior probability of a microbiome sample belonging to a disease or healthy category is calculated based on Bayes' theorem, using the likelihood values computed from the estimated Dirichlet-multinomial distribution, as well as a prior probability estimated from the training microbiome data

SUBMITTER: Gao X 

PROVIDER: S-EPMC5729222 | biostudies-literature | 2017 Nov-Dec

REPOSITORIES: biostudies-literature