Using the combination of rentrez, easyPubMed, pubmed.mineR, tokenizers and hugo academic template in R envoriment it was possible to analyze scientific literature and build up the PlatCOVID WebPortal. We use the LitCOVID database as the first curation process, as they report a increase in sensibility for COVID-19 literature. Then, we selected articles that had abstracts available. A secondary search was done according to five categories: diagnosis, treatment, epidemiology, transmission signs & symptoms (Box 1). The analysis of the abstracts was performed by the linguistic structured by the level of sentence and word tokenization using the pubmed.mineR and tokenizer, respectively. The common words were extract from the outcomes (Box 2).


  1. Chen Q, Allot A, Lu Z. Keep up with the latest coronavirus research. Nature. 2020;579(7798):193. doi:10.1038/d41586-020-00694-1

  2. Hugo Academic Template

  3. Rani J, Shah AB, Ramachandran S. pubmed.mineR: an R package with text-mining algorithms to analyse PubMed abstracts. J Biosci. 2015;40(4):671‐682. doi:10.1007/s12038-015-9552-2

  4. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL

  5. Winter, D. J. (2017) rentrez: an R package for the NCBI eUtils API. The R Journal 9(2):520-526

  1. Bivand RS, Pebesma E, Gomez-Rubio V (2013). Applied spatial data analysis with R, Second edition. Springer, NY.

  2. Tennekes M (2018). “tmap: Thematic Maps in R.” Journal of Statistical Software, 84(6), 1–39. doi: 10.18637/jss.v084.i06.