Machine learning and artificial intelligence in the multi-omics approach to gut microbiota

Abstract

The gut microbiome is involved in human health and disease, and its comprehensive understanding is necessary to exploit it as a diagnostic or therapeutic tool. Multi-omics approaches, including metagenomics, metatranscriptomics, metabolomics, and metaproteomics, enable depicting the complexity of the gut microbial ecosystem. However, these tools generate a large data stream, which integration is needed to produce clinically useful readouts but, in turn, might be difficult to carry out with conventional statistical methods.Artificial intelligence and machine learning have been increasingly applied to multi-omics datasets in several conditions associated with microbiome disruption, from chronic disorders to cancer. Such tools show potential for clinical implementation, including the discovery of microbial biomarkers for disease classification or prediction, the prediction of response to specific treatments, the fine-tuning of microbiome-modulating therapies. Here we discuss the state of the art, potential, and limits, of artificial intelligence and machine learning in the multi-omics approach to gut microbiome.