IEU DMER

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

Triangulating evidence in health sciences with Annotated Semantic Queries

Integrating information from data sources representing different study designs has the potential to strengthen evidence in population health research. In this work, led by Yi Liu, we present ASQ (Annotated Semantic Queries), a natural language query interface to EpiGraphDB, which enables users to annotate “claims” from a piece of unstructured text with evidence relevant to the claim.

EpiGraphDB platform version 1.0

EpiGraphDB v1.0 and summary of features and changes.

MendelVar: gene prioritization at GWAS loci using phenotypic enrichment of Mendelian disease genes

This paper, led by Maria Sobczyk, presented MendelVar, a tool which integrates knowledge from four databases on Mendelian disease genes with enrichment testing for a range of functional annotations to support the prioritization of genes at GWAS loci.

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.

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.