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Data Mining and Visualisation of PubMed

Visualisation and data mining of the public database of biomedical literature PubMed

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Updated 30 Oct 2019

GitHub view license on GitHub

Reference
Reyes-Aldasoro CC (2017) The proportion of cancer-related entries in PubMed has increased considerably; is cancer truly “The Emperor of All Maladies”? PLoS ONE 12(3): e0173671. https://doi.org/10.1371/journal.pone.0173671
Abstract
In this work, the public database of biomedical literature PubMed was mined using queries with combinations of keywords and year restrictions. It was found that the proportion of Cancer-related entries per year in PubMed has risen from around 6% in 1950 to more than 16% in 2016. This increase is not shared by other conditions such as AIDS, Malaria, Tuberculosis, Diabetes, Cardiovascular, Stroke and Infection some of which have, on the contrary, decreased as a proportion of the total entries per year. Organ-related queries were performed to analyse the variation of some specific cancers. A series of queries related to incidence, funding, and relationship with DNA, Computing and Mathematics, were performed to test correlation between the keywords, with the hope of elucidating the cause behind the rise of Cancer in PubMed. Interestingly, the proportion of Cancer-related entries that contain “DNA”, “Computational” or “Mathematical” have increased, which suggests that the impact of these scientific advances on Cancer has been stronger than in other conditions. It is important to highlight that the results obtained with the data mining approach here presented are limited to the presence or absence of the keywords on a single, yet extensive, database. Therefore, results should be observed with caution. All the data used for this work is publicly available through PubMed and the UK’s Office for National Statistics. All queries and figures were generated with the software platform Matlab and the files are available as supplementary material.

Cite As

Constantino Carlos Reyes-Aldasoro (2020). Data Mining and Visualisation of PubMed (https://www.github.com/reyesaldasoro/PubMed-Data-Mining), GitHub. Retrieved .

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