A Multi-gene-Feature-Based Genetic Algorithm for Prediction of Operon

  • Shuqin Wang
  • Yan Wang
  • Wei Du
  • Fangxun Sun
  • Xiumei Wang
  • Yanchun Liang
  • Chunguang Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4431)

Abstract

The prediction of operons is critical to reconstruction of regulatory networks at the whole genome level. In this paper, a multi-approach guided genetic algorithm is developed to prediction of operon. The fitness function is created by using intergenic distance of local entropy-minimization, participation of the same metabolic pathway, log-likelihood of COG gene functions and correlation coefficient of microarray expression data, which have been used individually for predicting operons. The gene pairs within operons have high fitness value by using these four scoring criteria, whereas those across transcription unit borders have low fitness value. On the other hand, it is easy to predict operons and makes the prediction ability stronger by using these four scoring criteria. The proposed method is examined on 683 known operons of Escherichia coli K12 and an accuracy of 85.9987% is obtained.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Shuqin Wang
    • 1
    • 2
  • Yan Wang
    • 1
  • Wei Du
    • 1
  • Fangxun Sun
    • 1
  • Xiumei Wang
    • 1
  • Yanchun Liang
    • 1
  • Chunguang Zhou
    • 1
  1. 1.College of Computer Science and Technology, Jilin University, Key Laboratory of, Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012China
  2. 2.School of Mathematics & Statistics, Northeast Normal University, Changchun, 130024China

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