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Mining the Bibliome

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Translational Informatics

Part of the book series: Health Informatics ((HI))

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Abstract

Biomedical literature offers a systematic catalogue of interpretations about data that can be used to infer new knowledge. The analysis of this literature (referred to in this chapter as the “bibliome”) in light of the exponential growth of biomedical data necessitates methodologies to transform data into knowledge. Such techniques are wrought with challenges, but offer some promise for transforming the big data deluge into novel hypotheses that can lead to new knowledge. This chapter provides an overview of the knowledge discovery process in the context of biomedical literature, and explains how such a process (referred to as “bibliome mining”) can be seen as an integral part of a learning healthcare system.

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Additional Reading

  • Collen MF. Computer medical databases: the first six decades (1950–2010). London: Springer; 2012. xix, 288 p.

    Google Scholar 

  • Danciu I, Cowan JD, Basford M, Wang X, Saip A, Osgood S, et al. Secondary use of clinical data: the Vanderbilt approach. J Biomed Inform. 2014. (in press) http://dx.doi.org/10.1016/j.jbi.2014.02.003

  • Friedman C. A broad-coverage natural language processing system. Proc AMIA Symp. 2000:270–4.

    Google Scholar 

  • Grivell L. Mining the bibliome: searching for a needle in a haystack? New computing tools are needed to effectively scan the growing amount of scientific literature for useful information. EMBO Rep. 2002;3(3):200–3.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Howe D, Costanzo M, Fey P, Gojobori T, Hannick L, Hide W, et al. Big data: the future of biocuration. Nature. 2008;455(7209):47–50.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Prokosch HU, Ganslandt T. Perspectives for medical informatics. Reusing the electronic medical record for clinical research. Methods Inf Med. 2009;48(1):38–44.

    CAS  PubMed  Google Scholar 

  • Salton G, McGill MJ. Introduction to modern information retrieval. New York: McGraw-Hill; 1983. xv, 448 p.

    Google Scholar 

  • Sarkar I. Methods in biomedical informatics: a pragmatic approach. Boston: Academic; 2013.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Center for Clinical and Translational Science, University of Vermont, Burlington, VT, USA

    Indra Neil Sarkar PhD, MLIS

  2. Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, VT, USA

    Indra Neil Sarkar PhD, MLIS

Authors
  1. Indra Neil Sarkar PhD, MLIS
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Correspondence to Indra Neil Sarkar PhD, MLIS .

Editor information

Editors and Affiliations

  1. Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA

    Philip R.O. Payne

  2. Biomedical Informatics, The Ohio State University Medical Center, Columbus, Ohio, USA

    Peter J. Embi

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© 2015 Springer-Verlag London

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Sarkar, I.N. (2015). Mining the Bibliome. In: Payne, P., Embi, P. (eds) Translational Informatics. Health Informatics. Springer, London. https://doi.org/10.1007/978-1-4471-4646-9_5

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  • DOI: https://doi.org/10.1007/978-1-4471-4646-9_5

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4645-2

  • Online ISBN: 978-1-4471-4646-9

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