Text Mining on Big and Complex Biomedical Literature

Text Mining on Big and Complex Biomedical Literature

Boya Xie (East Carolina University, USA), Qin Ding (East Carolina University, USA) and Di Wu (Drexel University, USA)
Copyright: © 2015 |Pages: 25
DOI: 10.4018/978-1-4666-6611-5.ch002
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Abstract

Driven by the rapidly advancing techniques and increasing interests in biology and medicine, about 2,000 to 4,000 references are added daily to MEDLINE, the US national biomedical bibliographic database. Even for a specific research topic, extracting useful and comprehensive information out of the huge literature data pool is challenging. Text mining techniques become extremely useful when dealing with the abundant biomedical information and they have been applied to various areas in the realm of biomedical research. Instead of providing a brief overview of all text mining techniques and every major biomedical text mining application, this chapter explores in-depth the microRNA profiling area and related text mining tools. As an illustrative example, one rule-based text mining system developed by the authors is discussed in detail. This chapter also includes the discussion of the challenges and potential research areas in biomedical text mining.
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Background

The knowledge and techniques in biomedicine have been advanced drastically. Collaboration among computer science, physics, mathematics, and engineering has enabled biomedical researchers to explore solutions to many of the world's most concerned health problems. Biomedicine research topics span from molecules to macro environment. Text mining has been applied to many of these data intensive areas. The most studied areas in biomedicine text-mining are discussed as follows.

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