Associations between the human gut microbiome and expression of host illness have been noted in a variety of conditions ranging from gastrointestinal dysfunctions to neurological deficits. Machine learning (ML) methods have generated promising results for disease prediction from gut metagenomic information for diseases including liver cirrhosis and irritable bowel disease, but have lacked efficacy when predicting other illnesses. Here, we review current ML methods designed for disease classification from microbiome data. We highlight the computational challenges these methods have effectively overcome and discuss the biological components that have been overlooked to offer perspectives on future work in this area.

The human gut microbiome is considered an organ in its entirety and has been the subject of extensive research due to its role in physiology, metabolism, digestion, and immune regulation. Disequilibria of the normal microbiome have been associated with the development of several gastrointestinal diseases, but the exact underlying interactions are not well understood. Conventional in vivo and in vitro modelling systems fail to faithfully recapitulate the complexity of the human host–gut microbiome, emphasising the requirement for novel systems that provide a platform to study human host–gut microbiome interactions with a more holistic representation of the human in vivo microenvironment. In this review, we outline the progression and applications of new and old modelling systems with particular focus on their ability to model and to study host–microbiome cross-talk.

Microbial communities of the urogenital tract have long been recognised to play an important role in disease states. A revolution in methodological approaches is permitting the assessment of complex urogenital tract microbiota–host interactions and the metabolic and protein milieu of the mucosal interface. There is now great potential for significant advances in biomarker discovery and disease risk stratification, and for the elucidation of mechanisms underpinning the microbial community dynamics involved in urogenital tract pathology. Microbiota–host interactions in the female genital tract have a particular significance, because unlike in the male, there is direct communication between the external genitalia, the uterus and the peritoneal cavity. This review examines the microbial community composition at differing sites of the female urogenital tract and its relationship with health and disease. Key factors involved in the modulation of vaginal microbiome stability and structure, such as endocrine, immune and inflammatory pathways, are considered in the context of a woman's life cycle and disease pathogenesis.