Publishing DisGeNET as nanopublications

Queralt-Rosiñach, Núria; Kuhn, Tobias; Chichester, Christine; Dumontier, Michel; Sanz, Ferrán; Furlong, Laura I.

doi:10.3233/sw-150189

Cited by 21 publications

(16 citation statements)

References 20 publications

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“…DisGeNET data are also published as machine-readable data through DisGeNET-RDF (23) and nanopublications (24) linked data sets, which increases the FAIRness of the data (25). Entities and properties in DisGeNET-RDF are semantically defined making extensive reuse of standard identifiers, vocabularies and ontologies such as the National Cancer Institute thesaurus, for medical vocabulary and the Semanticscience Integrated Ontology (26) for general science.…”

Section: Flexible Data Access To Answer Different Research Questionsmentioning

confidence: 99%

DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants

Piñero¹,

Bravo²,

et al. 2016

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The information about the genetic basis of human diseases lies at the heart of precision medicine and drug discovery. However, to realize its full potential to support these goals, several problems, such as fragmentation, heterogeneity, availability and different conceptualization of the data must be overcome. To provide the community with a resource free of these hurdles, we have developed DisGeNET (http://www.disgenet.org), one of the largest available collections of genes and variants involved in human diseases. DisGeNET integrates data from expert curated repositories, GWAS catalogues, animal models and the scientific literature. DisGeNET data are homogeneously annotated with controlled vocabularies and community-driven ontologies. Additionally, several original metrics are provided to assist the prioritization of genotype–phenotype relationships. The information is accessible through a web interface, a Cytoscape App, an RDF SPARQL endpoint, scripts in several programming languages and an R package. DisGeNET is a versatile platform that can be used for different research purposes including the investigation of the molecular underpinnings of specific human diseases and their comorbidities, the analysis of the properties of disease genes, the generation of hypothesis on drug therapeutic action and drug adverse effects, the validation of computationally predicted disease genes and the evaluation of text-mining methods performance.

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Section: Flexible Data Access To Answer Different Research Questionsmentioning

confidence: 99%

DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants

Piñero¹,

Bravo²,

et al. 2016

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“…Targets related to IS were derived from six public databases, including DisGeNET (http://www.disgenet.org/) [29], OMIM https://omim.org/ [28], TTD http://bidd.n us.edu.sg/group/cjttd/ , DrugBank https://www.drugbank.ca/ , PharmGKB http s://www.pharmgkb.org/ and MalaCards https://www.malacards.org/ [30] with key words "ischemic stroke".…”

Section: Chemical Ingredients Collection and Active Compounds Screeningmentioning

confidence: 99%

Network pharmacology and molecular docking analysis on molecular targets and mechanisms of Buyang Huanwu Decoction in the treatment of Ischemic Stroke

Gao

Tian

Han

et al. 2020

Preprint

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Background and objective: With the exact clinical efficacy, Buyang Huanwu decoction (BHD) is a classical prescription for the treatment of ischemic stroke (IS). Here, we aimed to investigate the pharmacological mechanisms of BHD in treating IS using systems biology approaches.Methods: The bioactive components and potential targets of BHD were screened by TCMSP, BATMAN-TCM, ETCM, and SymMap databases. Besides, compounds that failed to find the targets from the above databases were predicted through STITCH, SWISS, and SEA. Moreover, six databases were searched to mine targets of IS. The intersection targets were obtained, and analyzed by GO and KEGG enrichment. Furthermore, BHD-IS PPI network, compound-compound target-IS network and pathway of drug-compound target-IS network were constructed by Cytoscape 3.6.0. Finally, AutoDock was used for molecular docking verification.Results：A total of 253 putative targets were obtained from 60 active compounds in BHD. Among them, 62 targets were related to IS. PPI network showed that the top ten key targets were IL6, TNF, VEGFA, and AKT1, etc. The enrichment analysis demonstrated candidate BHD targets were more frequently involved TNF, PI3K-Akt, and NF-kappa B signaling pathway. Network topology analysis showed that Radix Astragali was the main herb in BHD, and the key components were quercetin, beta-Sitosterol, kaempferol, and stigmasterol, etc. The results of molecular docking showed the active components in BHD had a good binding ability with the key targets.Conclusions: This study firstly adopted the methods of network pharmacology and molecular docking to reveal the relationships among herbs in BHD, the putative targets and IS-related pathways.

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“…when and by whom it was created). Even though the details of their integration into the scientific publishing workflow have remained largely unspecified, nanopublications have received considerable attention during the last few years, with several large dataset having been published in this format [2,12,40]. Table 1 summarizes the different types of approaches introduced above, showing that they cover different types of semantic representations.…”

Section: Related Workmentioning

confidence: 99%

Genuine semantic publishing

Kuhn

Dumontier

2017

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Editor: Silvio Peroni (https://orcid.org/0000-0003-0530-4305) Solicited reviews: Sarven Capadisli (https://orcid.org/0000-0002-0880-9125); Francesco Osborne (https://orcid.org/0000-0001-6557-3131); Jodi Schneider (https://orcid.org/0000-0002-5098-5667) Closer inspection, however, reveals that these approaches are mostly not about publishing semantic representations, as the name seems to suggest. Rather, they take the processes and outcomes of the current narrative-based publishing system for granted and only work with already published papers. This includes approaches involving semantic annotations, semantic interlinking, semantic integration, and semantic discovery, but with the semantics coming into play only after the publication of the original article. While these are interesting and important approaches, they fall short of providing a vision to transcend the current publishing paradigm. We argue here for taking the term semantic publishing literally and work towards a vision of genuine semantic publishing, where computational tools and algorithms can help us with dealing with the wealth of human knowledge by letting researchers capture their research results with formal semantics from the start, as integral components of their publications. We argue that these semantic components should furthermore cover at least the main claims of the work, that they should originate from the authors themselves, and that they should be fine-grained and light-weight for optimized re-usability and minimized publication overhead. This paper is in fact not just advocating our concept, but is itself a genuine semantic publication, thereby demonstrating and illustrating our points.

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Publishing DisGeNET as nanopublications

Cited by 21 publications

References 20 publications

DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants

DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants

Network pharmacology and molecular docking analysis on molecular targets and mechanisms of Buyang Huanwu Decoction in the treatment of Ischemic Stroke

Genuine semantic publishing

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