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Journal of Computer Science and Technology

, Volume 25, Issue 1, pp 169–179 | Cite as

New Challenges for Biological Text-Mining in the Next Decade

  • Hong-Jie Dai
  • Yen-Ching Chang
  • Richard Tzong-Han Tsai
  • Wen-Lian Hsu
Regular Paper

Abstract

The massive flow of scholarly publications from traditional paper journals to online outlets has benefited biologists because of its ease to access. However, due to the sheer volume of available biological literature, researchers are finding it increasingly difficult to locate needed information. As a result, recent biology contests, notably JNLPBA and BioCreAtIvE, have focused on evaluating various methods in which the literature may be navigated. Among these methods, text-mining technology has shown the most promise. With recent advances in text-mining technology and the fact that publishers are now making the full texts of articles available in XML format, TMSs can be adapted to accelerate literature curation, maintain the integrity of information, and ensure proper linkage of data to other resources. Even so, several new challenges have emerged in relation to full text analysis, life-science terminology, complex relation extraction, and information fusion. These challenges must be overcome in order for text-mining to be more effective. In this paper, we identify the challenges, discuss how they might be overcome, and consider the resources that may be helpful in achieving that goal.

Keywords

bioinformatics database mining method and algorithm text mining 

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Copyright information

© Springer 2010

Authors and Affiliations

  • Hong-Jie Dai
    • 1
    • 2
  • Yen-Ching Chang
    • 1
  • Richard Tzong-Han Tsai
    • 3
  • Wen-Lian Hsu
    • 1
    • 2
  1. 1.Institute of Information Science“Academia Sinica”TaiwanChina
  2. 2.Department of Computer Science“National Tsing-Hua University”TaiwanChina
  3. 3.Department of Computer Science and EngineeringYuan Ze UniversityTaiwanChina

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