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DC Field | Value | Language |
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dc.contributor.author | Liu, Y | - |
dc.contributor.author | Elsworth, B | - |
dc.contributor.author | Erola, P | - |
dc.contributor.author | Haberland, V | - |
dc.contributor.author | Hemani, G | - |
dc.contributor.author | Lyon, M | - |
dc.contributor.author | Zheng, J | - |
dc.contributor.author | Lloyd, O | - |
dc.contributor.author | Vabistsevits, M | - |
dc.contributor.author | Gaunt, TR | - |
dc.date.accessioned | 2023-10-04T13:06:14Z | - |
dc.date.available | 2023-10-04T13:06:14Z | - |
dc.date.issued | 2020-11-24 | - |
dc.identifier | ORCID iDs: Yi Liu https://orcid.org/0000-0002-2051-440X; Valeriia Haberland https://orcid.org/0000-0002-3874-0683 | - |
dc.identifier.citation | Liu, Y. et al. (2021) 'EpiGraphDB: A database and data mining platform for health data science', Bioinformatics, 37 (9), pp. 1304 - 1311. doi: 10.1093/bioinformatics/btaa961. | en_US |
dc.identifier.issn | 1367-4803 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/27309 | - |
dc.description | The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors. | en_US |
dc.description | A correction has been published: Bioinformatics, Volume 37, Issue 2, January 2021, Page 288, https://doi.org/10.1093/bioinformatics/btab104 | - |
dc.description | Supplementary data is available online at https://academic.oup.com/bioinformatics/article/37/9/1304/5962087#supplementary-data . | - |
dc.description.abstract | Copyright © The Author(s) 2020. 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. | en_US |
dc.description.sponsorship | This work was supported by the UK Medical Research Council [MC_UU_00011/4]. J.Z. is a University of Bristol Vice-Chancellors Fellow. G.H. was funded by the Wellcome Trust and Royal Society [208806/Z/17/Z]. This work has also been supported by a Cancer Research UK programme grant [C18281/A19169] and British Heart Foundation Accelerator Award [AA/18/7/34219]. This work has also been supported by the NIHR Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol. T.R.G. and G.H. receive research funding from GlaxoSmithKline and Biogen. V.H. has previously been supported by funding from GlaxoSmithKline. | en_US |
dc.format.extent | 1304 - 1311 | - |
dc.format.medium | Print-Electronic | - |
dc.language.iso | en | en_US |
dc.publisher | Oxford University Press | en_US |
dc.rights | Copyright © The Author(s) 2020. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.title | EpiGraphDB: A database and data mining platform for health data science | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1093/bioinformatics/btaa961 | - |
dc.relation.isPartOf | Bioinformatics | - |
pubs.issue | 9 | - |
pubs.publication-status | Published | - |
pubs.volume | 37 | - |
dc.identifier.eissn | 1460-2059 | - |
dc.rights.holder | The Author(s) | - |
Appears in Collections: | Dept of Computer Science Research Papers |
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