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Methods. 2015 Mar;74:97-106. doi: 10.1016/j.ymeth.2015.01.015. Epub 2015 Jan 30.

Application of text mining in the biomedical domain.

Author information

1
Computational Discovery & Design Group CMBI, Radboudumc, Nijmegen, The Netherlands; Netherlands eScience Center, The Netherlands.
2
Computational Discovery & Design Group CMBI, Radboudumc, Nijmegen, The Netherlands; NIZO Food Research BV, Ede, The Netherlands. Electronic address: wynand.alkema@nizo.com.

Abstract

In recent years the amount of experimental data that is produced in biomedical research and the number of papers that are being published in this field have grown rapidly. In order to keep up to date with developments in their field of interest and to interpret the outcome of experiments in light of all available literature, researchers turn more and more to the use of automated literature mining. As a consequence, text mining tools have evolved considerably in number and quality and nowadays can be used to address a variety of research questions ranging from de novo drug target discovery to enhanced biological interpretation of the results from high throughput experiments. In this paper we introduce the most important techniques that are used for a text mining and give an overview of the text mining tools that are currently being used and the type of problems they are typically applied for.

KEYWORDS:

Automatic information extraction; Biomedical research; Drug discovery; Natural language processing; Ontology; Text mining

PMID:
25641519
DOI:
10.1016/j.ymeth.2015.01.015
[Indexed for MEDLINE]

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