Deciphering the gut microbiome: The revolution of artificial intelligence in microbiota analysis and intervention

Abstract

The human gut microbiome is an intricate ecosystem with profound implications for host metabolism, immune function, and neuroendocrine activity. Over the years, studies have strived to decode this microbial universe, especially its interactions with human health and underlying metabolic processes. Traditional analyses often struggle with the complex interplay within the microbiome due to presumptions of microbial independence. In response, machine learning (ML) and deep learning (DL) provide advanced multivariate and non-linear analytical tools that adeptly capture the complex interactions within the microbiota. With the influx of data from metagenomic next-generation sequencing (mNGS), there's an increasing reliance on these artificial intelligence (AI) subsets to derive actionable insights. This review delves deep into the cutting-edge ML techniques tailored for human gut microbiota research. It further underscores the potential of gut microbiota in shaping clinical diagnostics, prognosis, and intervention strategies, pointing to a future where computational methods bridge the gap between microbiome knowledge and targeted health interventions.