Uncovering longitudinal trajectories in molecular and clinical population data

This is one of several projects available on an MRC funded 4-year multi-disciplinary PhD programme in Human Genetics, Genomics and Disease at the MRC Human Genetics Unit (HGU), part of the Institute of Genetics and Molecular Medicine (IGMM) at the University of Edinburgh.

Project details:

Large population cohorts typically collect high-dimensional molecular data at a single time-point per subject. We will use pseudotime machine learning approaches to approximate longitudinal molecular trajectories from cross-sectional data, and explore extensions to jointly uncover molecular and clinical disease progression trajectories, incorporating electronic health records. For example, do pseudotime DNA methylation trajectories predict health outcomes such as stroke/cancer/dementia? Our methods will be applied to Generation Scotland (generationscotland.org), but are also highly relevant for other cohorts with linked molecular and clinical information.

This project would suit a motivated student with statistics, data science, machine learning, bioinformatics or related backgrounds.

For further information on how to apply for this project, please visit: https://www.ed.ac.uk/mrc-human-genetics-unit/graduate-research-and-training/mrc-four-year-phd-programme-quantitative-scientist