Your gut is home to trillions of bacteria — and unlocking their secrets may be key to better health. But how do you sort useful patterns from the chaos? One team thinks AI may be the answer.
In a new study published in Briefings in Bioinformatics, researchers at the University of Tokyo unveiled a specialized artificial intelligence system designed to find meaningful connections between gut microbes and the chemical signals they produce. These chemicals, called metabolites, influence everything from your immune system to your metabolism, mood and risk of chronic disease.
The tool, called VBayesMM, is a type of Bayesian neural network, which not only analyzes massive amounts of data but also estimates uncertainty in its predictions — something traditional models often fail to do. That’s especially important in microbiome research, where the complexity is staggering.
“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. “By accurately mapping these bacteria-chemical relationships, we could potentially develop personalized treatments. 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 challenge, Dang said, lies in the scale. There are vast numbers of bacteria and metabolites, and even more possible interactions between them. Gathering the data is hard enough — making sense of it is even harder.
“Our 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,” Dang said. “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.”
Though the system is computationally demanding, the researchers are optimistic that these barriers will ease with time. The current version works best with robust datasets about bacterial communities. It also assumes microbes act independently, a simplification of what is actually a highly interactive environment.
“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,” Dang said. “We also aim to make VBayesMM more robust when analyzing diverse patient populations, incorporating bacterial ‘family tree’ relationships to make better predictions, and further reducing the computational time needed for analysis. For clinical applications, the ultimate goal is identifying specific bacterial targets for treatments or dietary interventions that could actually help patients, moving from basic research toward practical medical applications.”
This research was partly supported by the Japan Society for the Promotion of Science and JST CREST, two of Japan’s top science funding agencies.