Wikidata as a knowledge graph for the life sciences

Waagmeester, Andra; Stupp, Gregory S.; Burgstaller-Muehlbacher, Sebastian; Good, Benjamin M.; Griffith, Malachi; Griffith, Obi L.; Hanspers, Kristina; Hermjakob, Henning; Hudson, Toby; Hybiske, Kevin; Keating, Sarah M.; Manske, Magnus; Mayers, Michael; Mietchen, Daniel; Mitraka, Elvira; Pico, Alexander; Putman, Tim; Riutta, Anders; Queralt-Rosiñach, Núria; Schriml, Lynn M.; Shafee, Thomas; Slenter, Denise; Stephan, Ralf; Thornton, Katherine; Tsueng, Ginger; Tu, Roger; Ul-Hasan, Sabah; Willighagen, Egon; Wu, Chunlei; Su, Andrew I.

doi:10.7554/elife.52614

Cited by 107 publications

(119 citation statements)

References 67 publications

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“…Wikidata (https://wikidata.org) is a general knowledge base which contains an array of biomedical data sources that have recently been reported (Waagmeester et al, 2020). In contrast to curated knowledge bases such as EpiGraphDB, Wikidata is developed through community driven efforts and bot automation, and incorporates extensive knowledge across a wide array of fields, including (but not limited to) a range of biomedical entities, with duplication and redundancy of entities inevitable.…”

Section: Discussionmentioning

confidence: 99%

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

Liu

Elsworth

Erola

et al. 2020

Preprint

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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.

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Section: Discussionmentioning

confidence: 99%

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

Liu

Elsworth

Erola

et al. 2020

Preprint

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“…{ "_id": "43740571" , "_score": 15.594226 , "accession": { "genomic": [ "MN908947.3" , "NC_045512.2" ], "protein": [ "QHD43419.1" , "YP_009724393.1" ] }, "entrezgene": "43740571" , "locus_tag": "GU280_gp05" , "name": "membrane glycoprotein" , "other_names": "membrane glycoprotein" , "refseq": { "genomic": "NC_045512.2" , "protein": "YP_009724393.1" }, "retired": 43560233 , "symbol": "M" , "taxid": 2697049 , "type_of_gene": "protein-coding" } The COVID-19 related pathways from WikiPathways COVID-19 Portal are added to Wikidata using the approach previously described (10) . For this, a dedicated repository has been set up to hold the GPML files, the internal WikiPathways file format, The GPML is converted into RDF files with the WikiPathways RDF generator (36) , while the files with author information are manually edited.…”

Section: Mygeneinfomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The Gene Wiki project has been tearing down the different research silos on genetics, biological processes, related diseases and associated drugs (10) . In contrast to legacy databases, where data models follow a relational data schema of connected tables, Wikidata ( https://wikidata.org/ ) uses statements to store facts (see Figure 1) (10)(11)(12)(13) . This model of statements aligns well with the RDF triple model of the semantic web and the content of Wikidata is also serialized as Resource Description Framework (RDF) triples (14,15) , acting as stepping stone for data resources to the semantic web.…”

Section: Introductionmentioning

confidence: 99%

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A protocol for adding knowledge to Wikidata, a case report

Waagmeester

Willighagen

et al. 2020

Preprint

Self Cite

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Pandemics, even more than other medical problems, require swift integration of knowledge. When caused by a new virus, understanding the underlying biology may help finding solutions. In a setting where there are a large number of loosely related projects and initiatives, we need common ground, also known as a “commons”. Wikidata, a public knowledge graph aligned with Wikipedia, is such a commons and uses unique identifiers to link knowledge in other knowledge bases However, Wikidata may not always have the right schema for the urgent questions. In this paper, we address this problem by showing how a data schema required for the integration can be modelled with entity schemas represented by Shape Expressions. As a telling example, we describe the process of aligning resources on the genomes and proteomes of the SARS-CoV-2 virus and related viruses as well as how Shape Expressions can be defined for Wikidata to model the knowledge, helping others studying the SARS-CoV-2 pandemic. How this model can be used to make data between various resources interoperable, is demonstrated by integrating data from NCBI Taxonomy, NCBI Genes, UniProt, and WikiPathways. Based on that model, a set of automated applications or bots were written for regular updates of these sources in Wikidata and added to a platform for automatically running these updates. Although this workflow is developed and applied in the context of the COVID-19 pandemic, to demonstrate its broader applicability it was also applied to other human coronaviruses (MERS, SARS, Human Coronavirus NL63, Human coronavirus 229E, Human coronavirus HKU1, Human coronavirus OC4).

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“…There are numerous studies in the literature that aimed to integrate the available biomedical data [1][2][3][4][5][6][7][8][9][10] . These studies provided useful tools and methods to the life-sciences research community; however, many of them miss important functionalities that prevent them from becoming widely adopted tools/services (Supplementary Information section 1).…”

Section: Mainmentioning

confidence: 99%

CROssBAR: Comprehensive Resource of Biomedical Relations with Deep Learning Applications and Knowledge Graph Representations

Doğan

Ataş²,

Joshi

et al. 2020

Preprint

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Systemic analysis of available large-scale biological and biomedical data is critical for developing novel and effective treatment approaches against both complex and infectious diseases. Owing to the fact that different sections of the biomedical data is produced by different organizations/institutions using various types of technologies, the data are scattered across individual computational resources, without any explicit relations/connections to each other, which greatly hinders the comprehensive multi-omics-based analysis of data. We aimed to address this issue by constructing a new biological and biomedical data resource, CROssBAR, a comprehensive system that integrates large-scale biomedical data from various resources and store them in a new NoSQL database, enrich these data with deep-learning-based prediction of relations between numerous biomedical entities, rigorously analyse the enriched data to obtain biologically meaningful modules and display them to users via easy-to-interpret, interactive and heterogenous knowledge graph (KG) representations within an open access, user-friendly and online web-service at https://crossbar.kansil.org. As a use-case study, we constructed CROssBAR COVID-19 KGs (available at: https://crossbar.kansil.org/covid_main.php) that incorporate relevant virus and host genes/proteins, interactions, pathways, phenotypes and other diseases, as well as known and completely new predicted drugs/compounds. Our COVID-19 graphs can be utilized for a systems-level evaluation of relevant virus-host protein interactions, mechanisms, phenotypic implications and potential interventions.

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Wikidata as a knowledge graph for the life sciences

Cited by 107 publications

References 67 publications

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

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

A protocol for adding knowledge to Wikidata, a case report

CROssBAR: Comprehensive Resource of Biomedical Relations with Deep Learning Applications and Knowledge Graph Representations

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