TY - JOUR
T1 - EpiGraphDB: a database and data mining platform for health data science
AU - Liu, Yi
AU - Elsworth, Benjamin
AU - Erola, Pau
AU - Haberland, Valeriia
AU - Hemani, Gibran
AU - Lyon, Matt
AU - Zheng, Jie
AU - Lloyd, Oliver
AU - Vabistsevits, Marina
AU - Gaunt, Tom R.
N1 - An erratum can be found at: https://doi.org/10.1093/bioinformatics/btab104
"Upon the original publication of this article, there was an error in the source code syntax under sub-section “2.2 Integration of epidemiological evidence” in the “Materials and methods” section. The source code syntax should read: “(e.g. (Gwas {trait: ‘Body mass index’})-[MR {beta, se, pval}]->(Gwas {trait: ‘Coronary heart disease’}))” instead of “ (e.g. [Gwas (trait: ‘Body mass index’)]-[MR {beta, se, pval}]->(Gwas {trait: ‘Coronary heart disease’})))”. This error has now been corrected. The Publisher apologizes for the error."
PY - 2021/5/1
Y1 - 2021/5/1
N2 - 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 support 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.
AB - 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 support 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.
UR - https://www.biorxiv.org/content/10.1101/2020.08.01.230193v1
UR - http://www.scopus.com/inward/record.url?scp=85108028288&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btaa961
DO - 10.1093/bioinformatics/btaa961
M3 - Article
VL - 37
SP - 1304
EP - 1311
JO - Bioinformatics
JF - Bioinformatics
SN - 1367-4803
IS - 9
ER -