Biomedical text mining and its applications in cancer research
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Publication Details
Author list: Zhu F., Patumcharoenpol P., Zhang C., Yang Y., Chan J., Meechai A., Vongsangnak W., Shen B.
Publisher: Elsevier
Publication year: 2013
Journal: Journal of Biomedical Informatics (1532-0464)
Volume number: 46
Issue number: 2
Start page: 200
End page: 211
Number of pages: 12
ISSN: 1532-0464
Languages: English-Great Britain (EN-GB)
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Abstract
Cancer is a malignant disease that has caused millions of human deaths. Its study has a long history of well over 100. years. There have been an enormous number of publications on cancer research. This integrated but unstructured biomedical text is of great value for cancer diagnostics, treatment, and prevention. The immense body and rapid growth of biomedical text on cancer has led to the appearance of a large number of text mining techniques aimed at extracting novel knowledge from scientific text. Biomedical text mining on cancer research is computationally automatic and high-throughput in nature. However, it is error-prone due to the complexity of natural language processing. In this review, we introduce the basic concepts underlying text mining and examine some frequently used algorithms, tools, and data sets, as well as assessing how much these algorithms have been utilized. We then discuss the current state-of-the-art text mining applications in cancer research and we also provide some resources for cancer text mining. With the development of systems biology, researchers tend to understand complex biomedical systems from a systems biology viewpoint. Thus, the full utilization of text mining to facilitate cancer systems biology research is fast becoming a major concern. To address this issue, we describe the general workflow of text mining in cancer systems biology and each phase of the workflow. We hope that this review can (i) provide a useful overview of the current work of this field; (ii) help researchers to choose text mining tools and datasets; and (iii) highlight how to apply text mining to assist cancer systems biology research. ฉ 2012 Elsevier Inc.
Keywords
Biomedical text, Systems biology
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