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BioMed Research International
Volume 2013, Article ID 853043, 9 pages
http://dx.doi.org/10.1155/2013/853043
Research Article

Gene Prioritization of Resistant Rice Gene against Xanthomas oryzae pv. oryzae by Using Text Mining Technologies

1College of Science, Huazhong Agricultural University, Wuhan 430070, Hubei, China
2Department of Chinese, Translation and Linguistics, City University of Hong Kong, Kowloon, Hong Kong
3The Halliday Centre for Intelligent Applications of Language Studies, City University of Hong Kong, Kowloon, Hong Kong
4College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China
5College of Life Science, Huazhong Agricultural University, Wuhan 430070, Hubei, China

Received 4 May 2013; Revised 26 October 2013; Accepted 10 November 2013

Academic Editor: Huiru Zheng

Copyright © 2013 Jingbo Xia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

To effectively assess the possibility of the unknown rice protein resistant to Xanthomonas oryzae pv. oryzae, a hybrid strategy is proposed to enhance gene prioritization by combining text mining technologies with a sequence-based approach. The text mining technique of term frequency inverse document frequency is used to measure the importance of distinguished terms which reflect biomedical activity in rice before candidate genes are screened and vital terms are produced. Afterwards, a built-in classifier under the chaos games representation algorithm is used to sieve the best possible candidate gene. Our experiment results show that the combination of these two methods achieves enhanced gene prioritization.