Advanced AI Methods for Analysing Nutritional Influences on Mental Health

Mental health conditions, such as depression and anxiety, affect millions globally, creating an urgent need for innovative strategies to improve care. While the impact of nutrition on brain function and mental well-being is increasingly recognized, uncovering how dietary patterns interact with genetic and pharmacological factors remains a challenge. This project seeks to tackle this complexity by combining machine learning (ML) and topological data analysis (TDA) to uncover new insights into the links between nutrition, genetics, and mental health.

TDA is a modern mathematical approach that uncovers patterns in complex, high-dimensional datasets. This enables researchers to identify global structures, such as clusters of individuals with similar mental health trajectories, and subtle, localized patterns, like specific dietary-genetic interactions. When paired with ML, which excels at uncovering predictive relationships, this project aims to create powerful models that inform tailored, evidence-based mental health interventions.

Students joining this project will become a part of innovative multidisciplinary effort at Queen’s University Belfast, exploring how data-driven approaches can unlock new solutions for mental health challenges, ultimately improving lives through personalized, science-backed care. The project will be run between the School of Electronics, Electrical Engineering and Computer Science and the Centre for Public Health. Students will benefit from a multidisciplinary training program designed to expand their skillsets. Those with backgrounds in health or bioinformatics will learn advanced AI, ML, and TDA techniques, while students from computing or mathematics will gain expertise in nutritional science, gene expression analysis, and epidemiology. This tailored approach ensures all participants are equipped to contribute meaningfully to this transformative research.

Click here to access the application form