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Omics and Literature Mining

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Bioinformatics for Omics Data

Part of the book series: Methods in Molecular Biology ((MIMB,volume 719))

Abstract

The measurement of the simultaneous expression values of thousands of genes or proteins from high throughput Omics platforms creates a large amount of data whose interpretation by inspection can be a daunting task. A major challenge of using such data is to translate these lists of genes/proteins into a better understanding of the underlying biological phenomena. We describe approaches to identify biological concepts in the form of Medical Subject Headings (MeSH terms) as extracted from MEDLINE that are significantly overrepresented within the identified gene set relative to those associated with the overall collection of genes on the underlying Omics platform. The method’s principle strength is its ability to simultaneously depict similarities that may exist at the level of biological structure, molecular function, physiology, genetics, and clinically manifest diseases, just as a single published article about a gene of interest may report findings within several of these same dimensions.

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Acknowledgements

The author would like to thank Craig Volker, Pankaj Agarwal, Liwen Liu, Tom White, Dilip Rajagopalan, William Reisdorf, Karen Kabnick, and David Searls for their contribution towards the development of this approach.

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Authors and Affiliations

  1. Computational Biology, Quantitative Sciences, GlaxoSmithKline, King of Prussia, PA, USA

    Vinod Kumar

Authors
  1. Vinod Kumar
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Correspondence to Vinod Kumar .

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Editors and Affiliations

  1. emergentec biodevelopment GmbH, Gersthofer Strasse 29-31, Vienna, 1180, Austria

    Bernd Mayer

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© 2011 Springer Science+Business Media, LLC

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Kumar, V. (2011). Omics and Literature Mining. In: Mayer, B. (eds) Bioinformatics for Omics Data. Methods in Molecular Biology, vol 719. Humana Press. https://doi.org/10.1007/978-1-61779-027-0_21

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  • DOI: https://doi.org/10.1007/978-1-61779-027-0_21

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  • Publisher Name: Humana Press

  • Print ISBN: 978-1-61779-026-3

  • Online ISBN: 978-1-61779-027-0

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