Abstract
The human microbiome, or collection of microbes living on and within us, changes over time. Linking these changes to the status of the human host is crucial to understanding how the microbiome influences a variety of human diseases. Due to the large scale and complexity of microbiome data, computational methods are essential. Existing computational methods for linking changes in the microbiome to the status of the human host are either unable to scale to large and complex microbiome data sets or cannot produce human-interpretable outputs. In this thesis, we present Microbiome Differentiable Interpretable Temporal Rule Engine (MDITRE), a new highly efficient method leveraging deep-learning technologies to derive human-interpretable rules that predict host status from longitudinal microbiome data. Using semi-synthetic and a large compendium of publicly available 16S rRNA amplicon and metagenomics sequencing data sets, we demonstrate that in almost all cases, MDITRE performs on par with or better than popular uninterpretable machine learning methods, and orders-of-magnitude faster than the prior interpretable technique. MDITRE also provides a graphical user interface, which we show through case studies can be used to derive biologically meaningful interpretations linking patterns of microbiome changes over time with host phenotypes. Our work has the potential to enable new insights into how changes in the microbiome over time maintain health or lead to disease in humans and facilitate the development of diagnostic tests based on the microbiome.