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

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Abstract

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 .

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Acknowledgements

The authors thank Smriti Sharma and Inna Mittal for using the package and providing valuable feedback on text-mining and for suggesting improvements to the algorithms.

This work was supported by Council of Scientific and Industrial Research grant BSC0122.

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Authors and Affiliations

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

    Jyoti Rani, Ab Rauf Shah & Srinivasan Ramachandran

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  1. Jyoti Rani
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  2. Ab Rauf Shah
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  3. Srinivasan Ramachandran
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Corresponding author

Correspondence to Srinivasan Ramachandran.

Additional information

Supplementary materials pertaining to this article are available on the Journal of Biosciences Website at http://www.ias.ac.in/jbiosci/oct2015/supp/Rani.pdf

[Rani J, Shah AR and Ramachandran S 2015 pubmed.mineR: An R package with text-mining algorithms to analyse PubMed abstracts. J. Biosci.] DOI 10.1007/s12038-015-9552-2

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Glossary

Association

A term used to denote ‘closeness’ in relationship between a pair of terms.

Concept

A word referring to how it works. Examples – diabetes education, self-management, depigmentation, autoimmune.

Corpus

A collection of documents. plural-corpora

Document summarization

A short summary of the document including the most important parts such as brief introduction and conclusion.

Pre-processing

The process of preparing for analysis using mathematical approaches or other search and display utilities. Examples - word tokenization, sentence tokenization.

Term

A word with exact meaning. Examples -patient, vitiligo, diabetes educator.

Term-document matrix

A numerical matrix where terms are in rows and documents are in columns and the cells contain frequencies of occurrence of terms in the documents.

Text classification

Classifying the documents under defined terms.

Themes

Subjects usually defined by terms and preferably non-overlapping.

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Rani, J., Shah, A.R. & Ramachandran, S. pubmed.mineR: An R package with text-mining algorithms to analyse PubMed abstracts. J Biosci 40, 671–682 (2015). https://doi.org/10.1007/s12038-015-9552-2

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  • DOI: https://doi.org/10.1007/s12038-015-9552-2

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