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NCSA Faculty Fellows Gain Nutritional Insights from Biomarkers

Close-up photograph of petri dishes with various bacteria and samples in red, yellow, brown, and orange

When one typically thinks of artificial intelligence (AI), they are more-likely thinking of robots and computer algorithms that take on human characteristics, but rapidly, AI is gaining a foothold in a vast array of fields, ranging from healthcare to human nutrition. To be on the cutting edge of research now means being able to integrate these relatively new tools, like neural networks and machine learning, into sciences that are less explicitly technological.

With support from the NCSA Faculty Fellow program, Ruoqing Zhu in the Department of Statistics collaborated with Leila Shinn and Hannah Holscher, researchers from the Department of Food Science and Human Nutrition (FSHN) at the University of Illinois at Urbana-Champaign (UIUC). Together, they worked to analyze fecal bacteria to gain an accurate understanding of biomarkers of food intake., The visual analytics team at NCSA provided their expertise in data representation by incorporating the study into OMIX, their custom tool for microbiome data, so that the research team could study the results of Zhu’s analysis.

In order to comb through the massive amount of biomarkers in fecal bacteria, the research project turned to the machine learning expertise of Zhu and NCSA. With their help, researchers were able to distill the number of these biomarkers down to just the most significant (between 15 and 22 biomarkers) that could accurately describe the foods consumed by the study participants. By cleaning and processing these data and using artificial intelligence to their advantage, the team was able to predict if an individual had consumed a certain food with up to 85% accuracy, just based off of these handful of biomarkers.

These results are very exciting because they demonstrate that gut microbes can be used as biomarkers for food intake. Since the foods we eat affect our health, it moves us closer to being able to make dietary recommendations that modify the gut microbiota to improve health.

Hanna Holscher, Illinois Nutrition Professor

In the future, big data, and knowing how to utilize it and learn from it, will be important components to researching a litany of topics, including human nutrition.

You can read the full paper here, published this month in the Journal of Nutrition. Read more:

URBANA, Ill. – As food makes its way through your digestive system, gut microbiota also get energy from the food. This creates a microbial footprint of what you’ve consumed. But the detective work to find these footprints includes wrangling huge sets of data.

After all, the human gut microbiota is a collection of trillions of bacteria that reside in the gut.

new study from University of Illinois scientists in food science and human nutrition and statistics shows fecal bacteria can be used to identify food intake with up to 85% accuracy. The scientists identified the bacterial footprints of food consumption using machine learning models and bioinformatics to study the large, complicated datasets created in microbiota studies.

Importantly, the researchers were able to narrow down the list of hundreds of bacteria to 15 to 23 unique bacteria that could differentiate between the foods consumed in the studies. Their work is a promising step in dietary biomarker research and may ultimately help inform personalized nutrition recommendations.

Read the full news release here, via ACES.


The National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign provides supercomputing and advanced digital resources for the nation’s science enterprise. At NCSA, University of Illinois faculty, staff, students and collaborators from around the globe use these resources to address research challenges for the benefit of science and society. NCSA has been advancing many of the world’s industry giants for over 35 years by bringing industry, researchers and students together to solve grand challenges at rapid speed and scale.

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