Gut bacteria are known to be a key factor in many health-related concerns.
TOKYO — For the first time, researchers from the University of Tokyo have used a special kind of artificial intelligence (AI) called a Bayesian neural network to probe a dataset on gut bacteria in order to find relationships that current analytical tools could not reliably identify.
Gut bacteria are known to be a key factor in many health-related concerns. The human body comprises about 30 trillion to 40 trillion cells, but your intestines contain about 100 trillion gut bacteria.
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“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 in a paper published in Briefings in Bioinformatics.
By accurately mapping these bacteria-chemical relationships, we could potentially develop personalised treatments, Dang mentioned. “Imagine being able to grow a specific bacterium to produce beneficial human metabolites or designing targeted therapies that modify these metabolites to treat diseases.”
The system, VBayesMM, automatically distinguishes the key players that significantly influence metabolites from the vast background of less relevant microbes, while also acknowledging uncertainty about the predicted relationships, rather than providing overconfident but potentially wrong answers.
“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,” Dang explained.
As VBayesMM can handle and communicate issues of uncertainty, it gives researchers more confidence than a tool which does not. Even though the system is optimised to cope with heavy analytical workloads, mining such huge datasets still comes with high computational cost; however, as time goes on, this will become less and less of a barrier to those wishing to use it.
“We plan to work with more comprehensive chemical datasets that capture the complete range of bacterial products, though this creates new challenges in determining whether chemicals come from bacteria, the human body or external sources like diet,” said Dang.