Application of text mining in the biomedical domain
Application of text mining in the biomedical domain
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DOI：
10.1016/j.ymeth.2015.01.015
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发表时间：
2015-03-01
期刊：
影响因子：
4.8
通讯作者：
Alkema, Wynand
中科院分区：
文献类型：
--
作者：
Fleuren, Wilco W. M.;Alkema, Wynand
近年来，生物医学研究中产生的实验数据量和在该领域发表的论文数量迅速增长。为了跟上他们感兴趣的领域的发展，并根据所有可用的文献解释实验结果，研究人员越来越多地转向使用自动化文献挖掘。因此，文本挖掘工具在数量和质量上都发生了相当大的发展，如今可用于解决各种研究问题，从从头发现药物靶点到增强对高通量实验结果的生物学解释。在本文中，我们将介绍用于文本挖掘的最重要的技术，并概述了目前正在使用的文本挖掘工具和它们通常适用的问题类型。(C)2015 Elsevier Inc. All rights reserved.
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. (C) 2015 Elsevier Inc. All rights reserved.