Vietnamese researcher uses AI to decode the "language" of intestinal bacteria

According to ScienceDaily, in a groundbreaking study, scientists in Japan have applied advanced artificial intelligence (AI) to decode the complex ecosystem of gut bacteria and the chemical signals between them.

A team at the University of Tokyo has developed a new Bayesian neural network called VBayesMM, which can detect real biological relationships rather than random correlations.

The system has outperformed traditional models in studies of obesity, sleep disorders and cancer.

Gut bacteria play a vital role in human health, influencing digestion, immunity and even mood.

The human body contains about 30,000-40,000 billion human cells, while the intestines alone have up to 100,000 billion bacterial cells, meaning we carry more bacterial cells than our own cells.

These microorganisms not only participate in digestion, but also produce and transform thousands of small compounds called metabolites – “chemical messengers” that influence metabolism, the immune system and brain function.

“We are only just beginning to understand which bacteria produce which metabolites and how these relationships change in different diseases,” said researcher Tung Dang (Đăng Thanh Tùng) of the Tsunoda Laboratory, Department of Biological Sciences, University of Tokyo. “If we can precisely map the interactions between bacteria and chemicals, we can develop personalized treatments – for example, cultivating a certain type of bacteria to produce a substance that is beneficial to health, or designing therapies that manipulate those substances to treat diseases.”

The problem lies in the sheer scale of the data: thousands of interacting bacterial species and compounds make it extremely difficult to find meaningful patterns.

To solve this, the team used AI with a Bayesian approach to detect the bacterial groups that actually influence each metabolite, and also calculated the confidence level of the predictions – helping to avoid misleading conclusions.

“When tested on real-world data on sleep disorders, obesity, and cancer, our model consistently outperformed existing methods and identified bacterial families that matched known biological processes. This gives us confidence that the system is detecting real biological relationships and not random statistical patterns,” Tung added.

The ability to quantify uncertainty helps VBayesMM provide more reliable information to scientists. However, analyzing large microbiology datasets is still computationally intensive, although this cost will decrease as processing technology improves.

The system performs best when the amount of bacterial data is larger than the amount of metabolite data; otherwise, the accuracy decreases. Additionally, VBayesMM still treats each bacterial species as an independent entity, when in reality they interact intricately with each other.

The team is now looking to expand the model to handle more comprehensive chemical datasets, including compounds from bacteria, the human body, and diet. They also want to incorporate “family trees” of bacterial species to improve prediction power and reduce computation time.

“The ultimate goal is to identify specific bacteria that could be targets for treatment or nutritional intervention, thereby moving from basic research to clinical application,” said Mr. Tung.

With this new AI tool, scientists are getting closer to tapping the potential of the gut microbiome to develop personalized medicine, opening up a path for more precise and effective healthcare in the future./.

(TTXVN/Vietnam+)

Source: https://www.vietnamplus.vn/nha-nghien-cuu-viet-dung-ai-giai-ma-ngon-ngu-vi-khuan-duong-ruot-post1076191.vnp