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EpiGraphDB: a database and data mining platform for health data science

By Yi Liu, Benjamin Elsworth, Pau Erola, Valeriia Haberland, Gib Hemani, Matt Lyon, Jie Zheng, Tom R Gaunt

Posted 04 Aug 2020
bioRxiv DOI: 10.1101/2020.08.01.230193 (published DOI: 10.1093/bioinformatics/btaa961)

Motivation: The wealth of data resources on human phenotypes, risk factors, molecular traits and therapeutic interventions presents new opportunities for population health sciences. These opportunities are paralleled by a growing need for data integration, curation and mining to increase research efficiency, reduce mis-inference and ensure reproducible research. Results: We developed EpiGraphDB (https://epigraphdb.org/), a graph database containing an array of different biomedical and epidemiological relationships and an analytical platform to sup-port their use in human population health data science. In addition, we present three case studies that illustrate the value of this platform. The first uses EpiGraphDB to evaluate potential pleiotropic relationships, addressing mis-inference in systematic causal analysis. In the second case study we illustrate how protein-protein interaction data offer opportunities to identify new drug targets. The final case study integrates causal inference using Mendelian randomization with relationships mined from the biomedical literature to "triangulate" evidence from different sources. Availability: The EpiGraphDB platform is openly available at https://epigraphdb.org. Code for repli-cating case study results is available at https://github.com/MRCIEU/epigraphdb as Jupyter notebooks using the API, and https://mrcieu.github.io/epigraphdb-r using the R package. ### Competing Interest Statement Tom Gaunt and Gibran Hemani receive research funding from GlaxoSmithKline and Biogen. Valeriia Haberland has previously been supported by funding from GlaxoSmithKline.

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