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Activity Number: 225 - The Human Microbiome: From Discovery Studies to Statistical Predictive Personalized Medicine
Type: Topic Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #304458
Title: Predictive Modeling of Microbial Community Data Using Phylogeny-Regularized Regression Models
Author(s): Jun Chen*
Companies: Mayo Clinic
Keywords: microbiome; phylogeny; high-dimensional statistics; generalized linear mixed model; sparse regression model; predictive modeling

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. One unique characteristic of microbiome data is the phylogenetic tree that relates all the microbial taxa. It has frequently been observed that a cluster or clusters of taxa are associated with an outcome due to shared biological functions (clustered signal). Depending on the specific condition, a large or a small number of taxa can be involved, representing two distinct biological models (dense and sparse model). We thus develop “glmmTree”, a phylogeny-regularized generalized linear mixed model, for clustered and dense signal, and “SICS”, a phylogeny-regularized sparse generalized linear model, for clustered and sparse signal, respectively. glmmTree uses the global phylogeny-based similarity between microbiomes to predict the outcome while SICS performs variable selection and uses a novel phylogeny-based smoothness penalty to smooth the coefficients of related microbial taxa. Simulation studies and real data applications were used to demonstrate the performance of the proposed method.

Authors who are presenting talks have a * after their name.

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