›› 2010, Vol. 25 ›› Issue (1): 169-inside back cover.

Special Issue: Data Management and Data Mining

• Special Issue on Computational Challenges from Modern Molecular Biology • Previous Articles    

New Challenges for Biological Text-Mining in the Next Decade

Hong-Jie Dai1,2, Yen-Ching Chang1, Richard Tzong-Han Tsai3, and Wen-Lian Hsu1,2, Fellow, IEEE   

  1. 1Institute of Information Science, "Academia Sinica'', 115, Taiwan, China
    2Department of Computer Science, "National Tsing-Hua University'', 300, Taiwan, China
    3Department of Computer Science and Engineering, Yuan Ze University, 320, Taiwan, China
  • Received:2009-09-01 Revised:2009-11-24 Online:2010-01-05 Published:2010-01-05
  • About author:
    Hong-Jie Dai received his B.S. degree in computer science and information engineering from Tung Hai University and his M.S. degree in computer science and information engineering from "National Central University in Taiwan'' in 2003 and 2005, respectively. Since 2005, he has been an assistant researcher at the "Academia Sinica''. His research interests include: bioinformatics, machine learning, text mining, natural language processing and software engineering.
    Yen-Ching Chang received her B.S. degree in biochemical science and technology from "National Taiwan University'' and her M.S. degree in biochemistry and molecular biology from "National Taiwan University'', College of Medicine. Since 2008, she has been an research assistant at the "Academia Sinica''. Her research interests include proteomics and text mining.
    Richard Tzong-Han Tsai received the B.S. degree in computer science and information engineering from "National Taiwan University'', Taipei, in 1997, the M.S. degree in computer science and information engineering from "National Taiwan University'' in 1999, and the Ph.D. degree in computer science and information engineering from "National Taiwan University'' in 2006. He was a postdoctoral fellow at "Academia Sinica'' from 2006 to 2007. He is now an assistant professor of Department of Computer Science and Engineering, Yuan Ze University, Zhongli, Taiwan, China. His research areas are natural language processing, cross-language information retrieval, biomedical literature mining, and information services on mobile devices.
    Wen-Lian Hsu is a distinguished research fellow in the Institute of Information Science, "Academia Sinica''. He received his B.S. degree from the Department of Mathematics, "National Taiwan University'' in 1973 and his Ph.D. degree in operations research from Cornell University in 1980, respectively. He then joined Northwestern University, and was promoted to tenured associate professor in 1986. In 1989, he joined the Institute of Information Science as a research fellow. Earlier in his career, Dr. Hsu focused on theoretical graph algorithms and frequently published papers in top-notch journals, such as JACM, SIAM Journal on Computing. After returning to Taiwan, China, he started research on automatic conversion of Pinyin to characters. In 1993, he invented a Chinese natural input method which has since attracted two million users and revolutionized the phonetic input for Chinese in Taiwan, China. Later, he moved into question answering, and bioinformatics. He is currently the director of the International Graduate Program in Bioinformatics in "Academia Sinica''. Dr. Hsu received many awards including the Distinguished Research Fellow Award of the "National Science Council'', K. T. Li breakthrough award, IEEE fellow, "Academia Sinica Investigator'' Award, and Teco Technology Award. From 2001 to 2002, he was the President of the Artificial Intelligence Society in Taiwan, China.
  • Supported by:

    This work was supported by the "National Science Council'' under Grant Nos. NSC 97-2218-E-155-001 and NSC96-2752-E-001-001-PAE, the Research Center for Humanities and Social Sciences, and the Thematic Program of "Academia Sinica'' under Grant No. AS95ASIA02.

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.

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