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PLoS Comput Biol. 2013 Apr;9(4):e1003044. doi: 10.1371/journal.pcbi.1003044. Epub 2013 Apr 25.

Chapter 16: text mining for translational bioinformatics.

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1
Computational Bioscience Program, University of Colorado School of Medicine, Aurora, Colorado, USA. kevin.cohen@gmail.com

Abstract

Text mining for translational bioinformatics is a new field with tremendous research potential. It is a subfield of biomedical natural language processing that concerns itself directly with the problem of relating basic biomedical research to clinical practice, and vice versa. Applications of text mining fall both into the category of T1 translational research-translating basic science results into new interventions-and T2 translational research, or translational research for public health. Potential use cases include better phenotyping of research subjects, and pharmacogenomic research. A variety of methods for evaluating text mining applications exist, including corpora, structured test suites, and post hoc judging. Two basic principles of linguistic structure are relevant for building text mining applications. One is that linguistic structure consists of multiple levels. The other is that every level of linguistic structure is characterized by ambiguity. There are two basic approaches to text mining: rule-based, also known as knowledge-based; and machine-learning-based, also known as statistical. Many systems are hybrids of the two approaches. Shared tasks have had a strong effect on the direction of the field. Like all translational bioinformatics software, text mining software for translational bioinformatics can be considered health-critical and should be subject to the strictest standards of quality assurance and software testing.

PMID:
23633944
PMCID:
PMC3635962
DOI:
10.1371/journal.pcbi.1003044
[Indexed for MEDLINE]
Free PMC Article
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