Building a PubMed knowledge graph
Date
2020Author
Xu, Jian
Kim, Sunkyu
Song, Min
Jeong, Minbyul
Kim, Donghyeon
Kang, Jaewoo
Rousseau, Justin F.
Li, Xin
Xu, Weijia
Torvik, Vetle I.
Bu, Yi
Chen, Chongyan
Akef Ebeid, Islam
Li, Daifeng
Ding, Ying
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Abstract
Experts in healthcare and medicine communicate in their own languages, such as SNOMED CT, ICD-10,
PubChem, and gene ontology. Tese languages equate to gibberish for laypeople, but for medical minds, they
are an intricate method of transporting important semantics and consensus capable of translating diagnoses,
medical procedures, and medications among millions of physicians, nurses, and medical researchers, thousands
of hospitals, hundreds of pharmacies, and a multitude of health insurance companies. Tese languages (e.g.,
genes, drugs, proteins, species, and mutations) are the backbone of quality healthcare. However, they are deeply
embedded in publications, making literature searches increasingly onerous because conventional text mining
tools and algorithms continue to be inefective. Given that medical domains are deeply divided, locating collaborators across domains is arduous. For instance, if a researcher wants to study ACE2 gene related to COVID-19,
he or she would like to know the following: which researchers are currently actively studying ACE2 gene, what
are the related genes, diseases, or drugs discussed in these articles related to ACE2 gene, and with whom could
the researcher collaborate? Tis is a strenuous position to be in, and the aforementioned problems diminish the
curiosity directed at the topic.
Palabras clave
COVID-19; PubMed knowledgeLink to resource
https://doi.org/10.1038/s41597-020-0543-2Collections
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