Publishing DisGeNET as nanopublications is a research paper published in Semantic Web (2016). On theSindex it has a DataRank of 1.0. It has been cited 34 times, with 23 citing works in its 1-hop citation network.
Abstract. The increasing and unprecedented publication rate in the biomedical field is a major bottleneck for discovery in Life Sciences. The scientific community cannot process assertions from biomedical publications and integrate them into the current knowledge at the same rate. The automatic extraction of assertions about entities and their relationships by text-mining the scientific literature is an extended approach to structure up-to-date knowledge. For knowledge integration, the publication of assertions in the Semantic Web is gaining adoption, but it opens new challenges regarding the tracking of the provenance, and how to ensure versioned data linking. Na-nopublications are a new way of publishing structured data that consists of an assertion along with its provenance. Trusty URIs is a novel approach to make resources in the Web immutable, and to ensure the unambiguity of the data linking in the (semantic) Web. We present the publication of DisGeNET nanopublications as a new Linked Dataset implemented in combination of the Trusty URIs approach. DisGeNET is a database of human gene-disease associations from expert-curated databases and text-mining the scientific literature. With a series of illus-trative queries we demonstrate its utility.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.533
From this paper's citation signal
Citation Network Contribution
0.505
From 12 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 51% comes from its base citations and 49% from the citation network (12 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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