pubmed.mineR: An R package with text-mining algorithms to analyse PubMed abstracts

Journal of biosciences

Srinivasan Ramachandran1¹  Ab Rauf Shah¹  Jyoti Rani¹ 

[1] GN Ramachandran Knowledge Centre for Genome Informatics, CSIR–Institute of Genomics and Integrative Biology, New Delhi 110 025, India$$

关键词: Package;    PubMed;    R;    text-mining;   

DOI  :  

来源: Indian Academy of Sciences

【 摘 要 】

The PubMed literature database is a valuable source of information for scientific research. It is rich in biomedical literature with more than 24 million citations. Data-mining of voluminous literature is a challenging task. Although several text-mining algorithms have been developed in recent years with focus on data visualization, they have limitations such as speed, are rigid and are not available in the open source. We have developed an R package, pubmed.mineR, wherein we have combined the advantages of existing algorithms, overcome their limitations, and offer user flexibility and link with other packages in Bioconductor and the Comprehensive R Network (CRAN) in order to expand the user capabilities for executing multifaceted approaches. Three case studies are presented, namely, `Evolving role of diabetes educators', `Cancer risk assessment' and `Dynamic concepts on disease and comorbidity' to illustrate the use of pubmed.mineR. The package generally runs fast with small elapsed times in regular workstations even on large corpus sizes and with compute intensive functions. The pubmed.mineR is available at http://cran.r- project.org/web/packages/pubmed.mineR.

【 授权许可】

Unknown