It takes guts to learn: machine learning techniques for disease detection from the gut microbiome

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.

Automated summary

Machine learning methods have shown promising results in disease prediction using gut microbiome data, particularly for liver cirrhosis and irritable bowel disease. However, challenges still exist in predicting other illnesses. Future research should focus on overcoming computational challenges and considering overlooked biological components in this area.