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Predictive Modeling Using Clinical Data And Compositional Microbiome Data, Li Zhang

All ETDs from UAB

This dissertation focuses on developing predictive modeling using data from two areas: clinical data and microbiome data. Compositional data in real life are mainly represented by relative proportions. With the advancement of next-generation sequencing (NGS) technology, researchers can now collect a large volume of metagenomic sequencing data, which is valuable for investigating associations between the microbiome and host diseases. Current methods for dealing with such data are either constrained by generalization or limited by application. In the first part, to address this, we propose Bayesian compositional generalized linear models for analyzing microbiome data (BCGLM). This model incorporates a structured regularized …