Transforming the Medical Subject Headings into Linked Data: Creating the Authorized Version of MeSH in RDF

Bushman, Barbara; Anderson, David P.; Fu, Gang

doi:10.1080/19386389.2015.1099967

Cited by 18 publications

(16 citation statements)

References 2 publications

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“…Through a manual inspection of such “identifier” communities (e.g., communities 1417 and 1632 in Table 2 ), we were able to detect different URI representations for similar entities (classes and instances). A few examples of different URI representations are shown in Table 3 for chemical entities of ChEBI 6 , PubChem 5 and MeSH 86 sources. Most LOD querying architectures rely on the existence of exact URIs to query multiple sources simultaneously (using data warehousing, link traversal or query federation methods) 13 , 17 , 40 .…”

Section: Discussionmentioning

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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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observe that several LSLOD sources exist as stand-alone data sources that are not inter-linked with other sources, use unpublished schemas with minimal reuse or mappings, and have elements that are not useful for data integration from a biomedical perspective.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: Discussionmentioning

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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“…The article is based on a breakout session held at the 38th UKSG Annual Conference in Glasgow on March-April 2015. Bushman, B., Anderson, D., & Fu, G. (2015). Transforming the Medical Subject Headings into Linked Data: Creating the authorized version of MeSH in RDF.…”

Section: Case Studies Experiments and Projectsmentioning

confidence: 99%

Linked Data in a Nutshell: A Starter Kit of Selected Annotated Bibliography and Resources for Academic Librarians: Part One

Liu

2018

IJoL

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It has been a decade since Tim Berners-Lee coined Linked Data in 2006. More and more Linked Data datasets have been made available for information retrieval on the Web. It is essential for librarians, especially academic librarians, to keep up with the state of Linked Data. There is so much information about Linked Data that one may wonder where to begin when they want to join the Linked Data community. With this in mind, the author compiled this annotated bibliography as a starter kit. Due to the many resources available, this list focuses on literature in English only and of specific projects, case studies, research studies, and tools that may be helpful to academic librarians, in addition to the overview of Linked Data concept and the current state of Linked Data evolution and adoption.

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“…Importantly, PubChemRDF enhances cross-integration by providing direct links to available authoritative RDF resources within applicable subdomains, including: reference, synonym, and InChIKey 86 to MeSH RDF 87 ; protein to UniProt RDF 88 ; protein and substance to PDB RDF 89 ; Biosystem to Reactome RDF 88 ; substance to ChEMBL RDF 88 ; and compound to WikiData RDF. 90 …”

Section: Automation Of Virtual Screening Pipelinesmentioning

confidence: 99%

Getting the most out of PubChem for virtual screening

Kim

2016

Expert Opinion on Drug Discovery

135

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Introduction With the emergence of the “big data” era, the biomedical research community has great interest in exploiting publicly available chemical information for drug discovery. PubChem is an example of public databases that provide a large amount of chemical information free of charge. Areas covered This article provides an overview of how PubChem’s data, tools, and services can be used for virtual screening and reviews recent publications that discuss important aspects of exploiting PubChem for drug discovery. Expert opinion PubChem offers comprehensive chemical information useful for drug discovery. It also provides multiple programmatic access routes, which are essential to build automated virtual screening pipelines that exploit PubChem data. In addition, PubChemRDF allows users to download PubChem data and load them into a local computing facility, facilitating data integration between PubChem and other resources. PubChem resources have been used in many studies for developing bioactivity and toxicity prediction models, discovering polypharmacologic (multi-target) ligands, and identifying new macromolecule targets of compounds (for drug-repurposing or off-target side effect prediction). These studies demonstrate the usefulness of PubChem as a key resource for computer-aided drug discovery and related area.

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Transforming the Medical Subject Headings into Linked Data: Creating the Authorized Version of MeSH in RDF

Cited by 18 publications

References 2 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

Linked Data in a Nutshell: A Starter Kit of Selected Annotated Bibliography and Resources for Academic Librarians: Part One

Getting the most out of PubChem for virtual screening

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