Bayesian compositional generalized linear mixed models for disease prediction using microbiome data

Bayesian compositional generalized linear mixed models for disease prediction using microbiome data

Abstract The primary goal of predictive modeling for compositional microbiome data is to better understand and predict disease susceptibility based on the relative abundance of microbial species. Current approaches in this area often assume a high-dimensional sparse setting, where only a small subse...

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Bibliographic Details
Main Authors: Li Zhang, Xinyan Zhang, Justin M. Leach, A. K. M. F. Rahman, Carrie R. Howell, Nengjun Yi
Format: Article
Language:English
Published: BMC 2025-04-01
Series:BMC Bioinformatics
Subjects:
Bayesian models
Compositional data
Mixed model
MCMC
Microbiome
Online Access:https://doi.org/10.1186/s12859-025-06114-3
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https://doi.org/10.1186/s12859-025-06114-3

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