Past Awardee

Machine Learning Approaches for Quantifying and Visualizing Dietary Effect in Personalized Nutrition

Ruoqing Zhu
Ruoqing Zhu

College: Liberal Arts and Sciences
Award year: 2020-2021
NCSA collaborators: Colleen Bushell, Peter Groves, Charles Blatti

Personalized nutrition is an emerging field that draws attention from the fields of molecular biology, machine learning and statistics. Scientists believe that diet can be used to modulate the microbiome (a collection of trillions of microorganisms that resides within the human gut) to help reduce the risk of developing diseases like Type 2 diabetes and cancer. Microbiome studies are "big data" studies but integration of these large, heterogeneous and "multi-omic" data has become increasingly important for understanding their association with dietary treatment. By studying the keystone species (the organism that helps hold the system together) and microbial signatures of "responders" versus "non-responders" participating in diet-microbiota-health clinical trials, which will inform the use of diet-microbiota-tailored treatments in precision nutrition efforts, the team will develop and verify a new computationally intensive analytical approach necessary to properly study the multi-omic data for the purpose of personalized nutrition, and identify new advanced visualization requirements for the interactive visualization tool.