Automated methods of predicting the function of biological sequences using GO and rough set

  • Authors:

  • Xu-Ning Tang;Zhi-Chao Lian;Zhi-Li Pei;Yan-Chun Liang

  • Affiliations:

  • College of Software, Jilin University, Changchun, China;College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Changchun, China;College of Computer Sci. and Techn., Jilin Univ., Key Lab. of Symbol Computation and Knowledge Eng. of Ministry of Education, Changchun, China and College of Mathematics and Comp. Sci., Inner Mong ...;College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Changchun, China

  • Venue:
  • PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
  • Year:
  • 2007

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Abstract

With the extraordinarily increase in genomic sequence data, there is a need to develop an effective and accurate method to deduce the biological functions of novel sequences with high accuracy. As the use of experiments to validate the function of biological sequence is too expensive and hardly to be applied to large-scale data, the use of computer for prediction of gene function has become an economical and effective substitute. This paper proposes a new design of BLAST-based GO term annotator which incorporates data mining techniques and utilizes rough set theory. Moreover, this method is an evolution against the traditional methods which only base on BLAST or characters of GO Terms. Finally, experimental results prove the validity of the proposed rough set-based method.