In a pioneering study, researchers from the University of Tokyo have used artificial intelligence (AI) to reveal previously undetectable links between gut bacteria and human health.
By employing a Bayesian neural network—a sophisticated form of AI—the team analysed vast gut microbiome datasets, uncovering biological patterns that conventional tools failed to identify.
The human gut hosts around 100 trillion bacteria, significantly more than the number of human cells.
These microbes play a crucial role in producing metabolites, the chemical compounds that influence health and disease.
However, mapping which bacteria produce which metabolites remains a major scientific challenge.
“The problem is that we’re only beginning to understand which bacteria produce which human metabolites and how these relationships change in different diseases,” said Project Researcher Tung Dang from the Tsunoda Lab in the Department of Biological Sciences.
The findings were published in the journal ‘Briefings in Bioinformatics.’
Introducing VBayesMM
To tackle this, the researchers created VBayesMM, a system designed to isolate key microbial contributors from a large background of less relevant bacteria.
Crucially, VBayesMM not only identifies these relationships but also accounts for uncertainty, providing scientists with more reliable insights instead of overconfident but potentially flawed predictions.
Success across multiple conditions
The AI tool was tested on datasets from studies on obesity, cancer, and sleep disorders.
In all cases, VBayesMM outperformed traditional methods, identifying bacterial families linked to known biological functions.
This alignment with existing biological knowledge suggests the AI is finding meaningful connections rather than random statistical coincidences.
“When tested on real data from sleep disorder, obesity and cancer studies, our approach consistently outperformed existing methods and identified specific bacterial families that align with known biological processes, giving confidence that it discovers real biological relationships rather than meaningless statistical patterns,” Project Researcher Tung Dang explained.
Future of personalised medicine
The implications of this technology could be far-reaching.
By better understanding which bacteria influence which metabolites, scientists may one day be able to design bespoke therapies—such as growing specific bacteria in the gut or adjusting metabolic pathways to treat disease.
The researchers now aim to work with more comprehensive datasets, capturing a wider range of bacterial products.
However, this introduces a new challenge: determining whether the metabolites come from bacteria, the human body, or external sources like food.
Although these tasks require intense computation, advances in technology will ease the process over time.
As Dang noted, “This creates new challenges, but also exciting opportunities for precision medicine.”
This study marks a major step toward understanding the human microbiome—and shows how AI can revolutionise health diagnostics and personalised treatments.
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