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

Systematic comparison of Mendelian randomization studies and randomized controlled trials using electronic databases

Triangulating results between Mendelian randomization studies and randomized controlled trials has the potential to strengthen evidence for an intervention target. In this work, led by Maria Sobczyk, we mined ClinicalTrials.Gov, PubMed and EpigraphDB databases and carried out a series of 26 manual literature comparisons among 54 MR and 77 RCT publications to explore the potential for systematic triangulation.

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.

Evaluating the potential benefits and pitfalls of combining protein and expression quantitative trait loci in evidencing drug targets

Molecular quantitative trait loci (molQTL), which can provide functional evidence on the mechanisms underlying phenotype-genotype associations, are increasingly used in drug target validation and safety assessment. In this work, led by Jamie Robinson, we evaluate the differences between expression and protein QTL and explore the possible reasons for apparent contradictory effects of genetic variants.