Paul Yousefi, PhD MPH (he/him) is a Research Fellow in Molecular and Epigenetic Epidemiology with the MRC Integrative Epidemiology Unit at the University of Bristol. He is interested in applying emerging methods in machine learning and statistical prediction to develop multi-dimensional genomic biomarkers of health risk factors, patterns of exposure, and emerging disease phenotypes.

He co-leads the ‘omics for prediction and prognosis’ workstream in the Translational Data Science theme of the NIHR Bristol Biomedical Research Centre (BRC) where he has ongoing projects related to identifying pleural malignancy and anticipating Parkinson’s disease progression trajectories. He also co-leads a work package on predictive biomarkers through the Cancer Research UK Integrative Cancer Epidemiology Programme (ICEP), with applications related to the detection of aggressive prostate cancer and lung cancer risk.

Paul completed his master’s degree (2011) and PhD (2016) at the University of California, Berkeley where his research focussed on statistical techniques for bias reduction in epigenome-wide association studies, and the impact of exposure to environmental pesticides and flame-retardant chemicals on DNA methylation.

View all research projects

Exploring inflammation as a driver for post-operative complications

One in seven patients develop a serious medical problem after surgery. These types of complications…

Theme Translational data science

Workstream Omics for prediction and prognosis

Identifying biomarkers for treatment response and disease prognosis

The aim of this project is to identify biomarkers that could help us predict how…

Theme Mental health

Workstream Biological interventions, trial recruitment and safety

Can we use DNA methylation to predict disease in diverse populations?

DNA methylation is a process during which methyl groups become attached to parts of our…

Theme Translational data science

Workstream Omics for prediction and prognosis

Using biomarkers and machine learning to predict antidepressant resistance

Around half of patients with depression don’t improve after taking antidepressants. Clinicians need to…

Theme Translational data science

Workstream Omics for prediction and prognosis

Can DNA methylation biomarkers predict whether pleural effusion is caused by cancer?

Pleural effusion, where fluid builds up in the cavity around the lungs, can develop…

Theme Translational data science

Workstream Omics for prediction and prognosis

Using DNA methylation biomarkers to understand Parkinson’s disease severity and progression

The Biogen Tel Aviv Parkinson Project (BeatPD) looks in-depth at clinical and genetic information…

Theme Translational data science

Workstream Omics for prediction and prognosis