Predictive Modeling of Microbiome Data Using a Phylogeny-Regularized Generalized Linear Mixed Model.

ABSTRACT: Recent human microbiome studies have revealed an essential role of the human microbiome in health and disease, opening up the possibility of building microbiome-based predictive models for individualized medicine. One unique characteristic of microbiome data is the existence of a phylogenetic tree that relates all the microbial species. It has frequently been observed that a cluster or clusters of bacteria at varying phylogenetic depths are associated with some clinical or biological outcome due to shared biological function (clustered signal). Moreover, in many cases, we observe a community-level change, where a large number of functionally interdependent species are associated with the outcome (dense signal). We thus develop "glmmTree," a prediction method based on a genera

SUBMITTER: Xiao J 

PROVIDER: S-EPMC6030386 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature