Daijiworld Media Network - Tokyo

Tokyo, Jul 6: In a groundbreaking development, scientists from the University of Tokyo have harnessed the power of a specialized artificial intelligence model—called a Bayesian neural network—to uncover previously hidden relationships between gut bacteria and human metabolites.

The tool, named VBayesMM, was developed by the Tsunoda Lab and tested on complex datasets related to sleep disorders, obesity, and cancer.

Unlike traditional statistical models, VBayesMM can distinguish between bacteria that meaningfully affect human chemistry and those that do not, all while accounting for uncertainty in its predictions.

“We’re only beginning to understand which bacteria produce which human metabolites,” said Project Researcher Tung Dang. “By accurately mapping these relationships, we can envision personalised treatments that grow or modify specific gut microbes to improve health outcomes.”

Gut bacteria play an outsized role in human health. While the body has around 30–40 trillion cells, the gut alone houses about 100 trillion bacteria. However, understanding which bacteria are responsible for which biological effects remains a massive scientific challenge.

Traditional analysis methods often struggle to find meaningful patterns in gut microbiome data without overfitting or producing unreliable results. VBayesMM solves this by factoring in uncertainty and handling complex, high-dimensional data with more nuance.

“When tested, our model consistently aligned its findings with known biological processes, unlike many other tools that identify statistically significant but biologically irrelevant patterns,” Dang noted.

Despite its effectiveness, VBayesMM requires significant computing power—though the team believes these barriers will shrink over time as technology evolves.

Looking ahead, the researchers plan to expand their analysis to larger and more diverse chemical datasets. “We aim to determine not just what chemicals are present, but whether they originate from bacteria, the body, or external sources like diet,” said Dang.

The findings, published in Briefings in Bioinformatics, pave the way for a new era of microbiome-based diagnostics and treatments.