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I lead the “Data Mining Epidemiological Relationships” programme in the MRC Integrative Epidemiology Unit at the University of Bristol. We are interested in understanding the mechanisms of disease, and approach this through the integration of diverse biomedical and epidemiological data and the development of software tools for analysis of these data. One of our key developments is EpiGraphDB, a database and software platform that integrates epidemiological and biomedical data to support mechanism discovery and aid causal inference.

We are always interested in hearing from potential collaborators or from talented graduate or postgraduate researchers who wish to pursue a career in software engineering applied to bioinformatics, molecular epidemiology or data science.

📫 Get in touch!

The following software platforms have been developed by members of my research group, others within the MRC IEU and our collaborators. Most are hosted on the MRC IEU GitHub organisation .

• EpiGraphDB integrates epidemiological and biomedical data to support mechanistic and causal inference.
• IEU OpenGWAS database contains thousands of publicly available GWAS for download or use in MR-Base.
• Vectology uses sentence embedding methods to compare distances between biomedical terms.
• MR-Base enables online Mendelian randomization analysis using a comprehensive manually curated database of GWAS studies.
• MELODI Presto is a literature mining platform for mechanistic inference.
• LD Hub supports online LD score regression analysis using a comprehensive manually curated database of GWAS studies.
• MELODI is a literature mining platform to identify potential mechanistic pathways between exposures and disease outcomes.
• FATHMM predicts the functional effects of genetic variants in coding and non-coding parts of the genome.
• MendelVar performs enrichment of ontology terms amongst genes linked to Mendelian disorders within genomic intervals.
• mQTLdb is a database of methylation QTL (mQTL) from different stages across the lifecourse.
• Genome Tolerance Browser visually compares functional prediction algorithms across the genome.
• HIPred is an integrative approach to predicting haploinsufficient genes.