Emerging Practices for Mapping and Linking Life Sciences Data Using RDF - A Case Series

Marshall, M. Scott; Boyce, Richard D.; Deus, Helena F.; Zhao, Jun; Willighagen, Egon; Samwald, Matthias; Pichler, Elgar; Hajagos, Janos; Pruddhommeaux, Eric; Stephens, Susie

doi:10.2139/ssrn.3198960

Cited by 8 publications

(14 citation statements)

References 29 publications

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“…In general, classes, properties, and instances in RDF graphs may be cross-linked with elements from external ontologies and vocabularies, as well as with entities from other LOD graphs, and making such linkages is often considered a best practice when modeling and publishing datasets on the LOD cloud 41 . For example, kegg:D01441 (G leevec ) of the KEGG RDF graph is linked to a similar entity drugbank:DB00619 of the DrugBank RDF graph, and kegg:map04020 (Ca2 + S ignalin g P athway ) is mapped to the similar class GO:0019722 in the Gene Ontology ( Fig.…”

Section: Background and Related Workmentioning

confidence: 99%

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An empirical meta-analysis of the life sciences linked open data on the web

Kamdar

Musen

2021

Sci Data

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While the biomedical community has published several “open data” sources in the last decade, most researchers still endure severe logistical and technical challenges to discover, query, and integrate heterogeneous data and knowledge from multiple sources. To tackle these challenges, the community has experimented with Semantic Web and linked data technologies to create the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we extract schemas from more than 80 biomedical linked open data sources into an LSLOD schema graph and conduct an empirical meta-analysis to evaluate the extent of semantic heterogeneity across the LSLOD cloud. We observe that several LSLOD sources exist as stand-alone data sources that are not inter-linked with other sources, use unpublished schemas with minimal reuse or mappings, and have elements that are not useful for data integration from a biomedical perspective. We envision that the LSLOD schema graph and the findings from this research will aid researchers who wish to query and integrate data and knowledge from multiple biomedical sources simultaneously on the Web.

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Section: Background and Related Workmentioning

confidence: 99%

“…Entity reconciliation and integrated querying are major problems in biomedicine, as there is often no agreement on a unique representation of a given entity or a class of entities 41 . Many biomedical entities are referred to by multiple labels, and the same labels may be used to refer to different entities.…”

Section: Background and Related Workmentioning

confidence: 99%

An empirical meta-analysis of the life sciences linked open data on the web

Kamdar

Musen

2021

Sci Data

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“…These scripts are Open Source and are available from the source code hosting service, GitHub [11]. This process has been outlined in a best practices note by the W3C Health Care and Life Sciences Interest Group [12]. This process is similar to its original description.…”

Section: Methodsmentioning

confidence: 99%

The ChEMBL database as linked open data

et al. 2013

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BackgroundMaking data available as Linked Data using Resource Description Framework (RDF) promotes integration with other web resources. RDF documents can natively link to related data, and others can link back using Uniform Resource Identifiers (URIs). RDF makes the data machine-readable and uses extensible vocabularies for additional information, making it easier to scale up inference and data analysis.ResultsThis paper describes recent developments in an ongoing project converting data from the ChEMBL database into RDF triples. Relative to earlier versions, this updated version of ChEMBL-RDF uses recently introduced ontologies, including CHEMINF and CiTO; exposes more information from the database; and is now available as dereferencable, linked data. To demonstrate these new features, we present novel use cases showing further integration with other web resources, including Bio2RDF, Chem2Bio2RDF, and ChemSpider, and showing the use of standard ontologies for querying.ConclusionsWe have illustrated the advantages of using open standards and ontologies to link the ChEMBL database to other databases. Using those links and the knowledge encoded in standards and ontologies, the ChEMBL-RDF resource creates a foundation for integrated semantic web cheminformatics applications, such as the presented decision support.

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“…However, given the large dataset we begin with, we are still able to identify large enough cohorts for the study. Drug recognition is done in a similar manner using strings from RxNORM and an independent study at the University of Pittsburgh, which examined the annotations on 1960 clinical notes manually [26], estimated over 84% sensitivity and 84% specificity for recognizing drugs.…”

Section: Methodsmentioning

confidence: 99%

Practice-Based Evidence: Profiling the Safety of Cilostazol by Text-Mining of Clinical Notes

et al. 2013

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BackgroundPeripheral arterial disease (PAD) is a growing problem with few available therapies. Cilostazol is the only FDA-approved medication with a class I indication for intermittent claudication, but carries a black box warning due to concerns for increased cardiovascular mortality. To assess the validity of this black box warning, we employed a novel text-analytics pipeline to quantify the adverse events associated with Cilostazol use in a clinical setting, including patients with congestive heart failure (CHF).Methods and ResultsWe analyzed the electronic medical records of 1.8 million subjects from the Stanford clinical data warehouse spanning 18 years using a novel text-mining/statistical analytics pipeline. We identified 232 PAD patients taking Cilostazol and created a control group of 1,160 PAD patients not taking this drug using 1∶5 propensity-score matching. Over a mean follow up of 4.2 years, we observed no association between Cilostazol use and any major adverse cardiovascular event including stroke (OR = 1.13, CI [0.82, 1.55]), myocardial infarction (OR = 1.00, CI [0.71, 1.39]), or death (OR = 0.86, CI [0.63, 1.18]). Cilostazol was not associated with an increase in any arrhythmic complication. We also identified a subset of CHF patients who were prescribed Cilostazol despite its black box warning, and found that it did not increase mortality in this high-risk group of patients.ConclusionsThis proof of principle study shows the potential of text-analytics to mine clinical data warehouses to uncover ‘natural experiments’ such as the use of Cilostazol in CHF patients. We envision this method will have broad applications for examining difficult to test clinical hypotheses and to aid in post-marketing drug safety surveillance. Moreover, our observations argue for a prospective study to examine the validity of a drug safety warning that may be unnecessarily limiting the use of an efficacious therapy.

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Emerging Practices for Mapping and Linking Life Sciences Data Using RDF - A Case Series

Cited by 8 publications

References 29 publications

An empirical meta-analysis of the life sciences linked open data on the web

An empirical meta-analysis of the life sciences linked open data on the web

The ChEMBL database as linked open data

Practice-Based Evidence: Profiling the Safety of Cilostazol by Text-Mining of Clinical Notes

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