The Schmidt Lab focuses on the computational analysis of biomedical data. We consider biomedical data in its full breadth: human genetics and tissue-specific omics; real-world data on prescriptions, diagnoses, and procedures; organ-specific imaging and physiological measurements; and curated aggregate data on pathogenic genetic variants, drug compounds, and tissue-specific expression.

For this work, we have access to biobank-scale data, electronic healthcare records, population cohort studies, and clinical datasets.

Researchers within the lab focus on cardiovascular disease (CVD), as well as diseases and conditions that predispose individuals to progressive CVD. Recognising that individuals with established CVD are at increased risk of developing disease in non-cardiac organs, we also conduct research on cardiometabolic and neurodegenerative diseases, as well as multimorbidity more broadly.

We routinely apply computational methods such as:

• Genome-wide association studies and Mendelian randomisation,
• Causal methods for pharmacoepidemiology, such as target trial emulation, marginal structural models and propensity scores,
• Supervised and unsupervised machine learning and regression
• Deep learning for segmentation and classification
• Systematic reviews and meta-analyses

The lab is led by Professor A. Floriaan Schmidt and is based across University College London (UCL) and Amsterdam University Medical Center (AUMC).