Literature DB >> 25641519

Application of text mining in the biomedical domain.

Wilco W M Fleuren1, Wynand Alkema2.   

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.
Copyright © 2015 Elsevier Inc. All rights reserved.

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

Mesh:

Year:  2015        PMID: 25641519     DOI: 10.1016/j.ymeth.2015.01.015

Source DB:  PubMed          Journal:  Methods        ISSN: 1046-2023            Impact factor:   3.608


  37 in total

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7.  Text mining for identification of biological entities related to antibiotic resistant organisms.

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Review 9.  Machine learning for molecular and materials science.

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