Bioinformatics pp 471-491 | Cite as

Text Mining

  • Andrew B. Clegg
  • Adrian J. Shepherd
Part of the Methods in Molecular Biology™ book series (MIMB, volume 453)

Abstract

One of the fastest-growing fields in bioinformatics is text mining: the application of natural language processing techniques to problems of knowledge management and discovery, using large collections of biological or biomedical text such as MEDLINE. The techniques used in text mining range from the very simple (e.g., the inference of relationships between genes from frequent proximity in documents) to the complex and computationally intensive (e.g., the analysis of sentence structures with parsers in order to extract facts about protein —protein interactions from statements in the text).

This chapter presents a general introduction to some of the key principles and challenges of natural language processing, and introduces some of the tools available to end-users and developers. A case study describes the construction and testing of a simple tool designed to tackle a task that is crucial to almost any application of text mining in bioinformatics —identifying gene/protein names in text and mapping them onto records in an external database.

Key words

Text mining natural language processing part-of-speech tagging named entity recognition parsing information retrieval information extraction 

Notes

Acknowledgments

This work was supported by the Biotechnology and Biological Sciences Research Council and AstraZeneca. The authors thank Mark Halling-Brown for supplying the dictionary and A. G. McDowell for implementing (and advising on) the Aho-Corasick algorithm.

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Copyright information

© Humana Press, a part of Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Andrew B. Clegg
    • 1
  • Adrian J. Shepherd
    • 1
  1. 1.Institute of Structural Molecular Biology, School of Crystallography, Birkbeck CollegeUniversity of LondonLondonUnited Kingdom

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