MRC IEU: Data Mining Epidemiological Relationships

The “Data Mining Epidemiological Relationships” programme, led by Prof Tom Gaunt, is funded by the UK Medical Research Council as part of 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 that integrates epidemiological and biomedical data to support mechanism discovery and aid causal inference. Read more...

Recent posts

Neo4J data integration pipeline

We make extensive use of Neo4J for graph databases (including EpiGraphDB). One of the key challenges in constructing a heterogeneous graph database is the data integration from different sources. Ben Elsworth describes the pipeline he has developed to automate this process.

Reducing drug development costs

An animation

Explaining our work in a way that is accessible to a wide audience is often challenging. Here we summarise some of our approaches to drug target prioritization in a short animation.

Visualising Brexit’s Impact on Food Safety in Britain

PhD students Marina Vabistsevits and Ollie Lloyd entereed the Jean Golding Institute data visualization competition on food hazards from around the world. Here they present their visualizations and interpretation, which won them a runner-up prize.

Drug target prioritization using protein QTL

A lot of our research recently has focused on drug target prioritization using Mendelian randomization and genetic colocalization. Here we introduce Jie (Chris) Zheng's Nature Genetics paper which describes our systematic analysis of the plasma proteome, part of an ongoing collaboration with pharma partners.

Exploring Elasticsearch architectures with Oracle Cloud

The IEU OpenGWAS database contains well over 100 billion rows of data on genetic associations. Ben Elsworth describes his work on implementing a cloud-based ElasticSearch database on the Oracle Cloud Infrastructure to can handle millions of queries per week.