Machine learning in microbiome research: methods, applications, and open challenges

Abstract

Microbiome research aims to understand the composition, diversity, and function of microbial communities and their interactions with their host organisms. As only a fraction of microbial species can be traditionally isolated and cultivated, advancements in high-throughput technologies have made it possible to generate large-scale microbiome datasets. The computational strength of artificial intelligence has helped in analysing these large sums of data. In particular, machine learning is a subfield of AI that has been widely utilized in microbiome studies. In this review, we provide an overview of machine learning and how it is utilized in microbiome research. We discuss ideas, new insights, open challenges, and future perspectives of machine learning in microbiome research. We suggest that collaborative efforts between microbiologists, bioinformaticians, and data scientists will be crucial to leveraging machine learning effectively for microbiome research. Despite its current drawbacks, machine learning shows tremendous potential for advancements in fields such as medicine, agriculture, and ecology.