Artificial Intelligence in Medicine
Volume 41, Issue 2 , Pages 151-159 , October 2007

A multi-approaches-guided genetic algorithm with application to operon prediction

  • Shuqin Wang

      Affiliations

    • College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China
    • School of Mathematics & Statistics, Northeast Normal University, Key Laboratory for Applied Statistics of the Ministry of Education, Changchun 130024, China
  • ,
  • Yan Wang

      Affiliations

    • College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China
  • ,
  • Wei Du

      Affiliations

    • College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China
  • ,
  • Fangxun Sun

      Affiliations

    • College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China
  • ,
  • Xiumei Wang

      Affiliations

    • College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China
  • ,
  • Chunguang Zhou

      Affiliations

    • College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China
  • ,
  • Yanchun Liang

      Affiliations

    • College of Computer Science and Technology, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China
    • Corresponding Author InformationCorresponding author. Tel.: +86 431 85153829; fax: +86 431 85168752.

Received 30 November 2006 ,Revised 30 July 2007 ,Accepted 30 July 2007.

References 

  1. Yeh P, Tschumi AI, Kishony R. Functional classification of drugs by properties of their pairwise interactions. Nat Genet. 2006;38:489–494
  2. Aloy P, Russell RB. Structural systems biology: modelling protein interactions. Nat Rev Mol Cell Biol. 2006;7:188–197
  3. Gon S, Camara JE, Klungsoyr HK, Crooke E, Skarstad K, Beckwith J. A novel regulatory mechanism couples deoxyribonucleotide synthesis and DNA replication in Escherichia coli. EMBO J. 2006;25:1137–1147
  4. Chen X, Su Z, Dam P, Palenik B, Xu Y, Jiang T. Operon prediction by comparative genomics: an application to the Synechococcus sp. WH8102 genome. Nucleic Acids Res. 2004;32:2147–2157
  5. Yada T, Nakao M, Totoki Y, Nakai K. Modeling and predicting transcriptional units of Escherichia coli genes using Hidden Markov models. Bioinformatics. 1999;15:987–993
  6. Salgado H, Moreno-Hagelsieb G, Smith T, Collado-Vides J. Operons in Escherichia coli: genomic analyses and predictions. Proc Natl Acad Sci. 2000;97:6652–6657
  7. Overbeek R, Fonstein M, D'Souza M, Pusch GD, Maltsev N. The use of gene clusters to infer functional coupling. Proc Natl Acad Sci. 1999;96:2896–2901
  8. Zheng Y, Szustakowski JD, Fortnow L, Roberts RJ, Kasif S. Computational identification of operons in microbial genomes. Genome Res. 2002;12:1221–1230
  9. Sabatti C, Rohlin L, Oh MK, Liao JC. Co-expression pattern from DNA microarray experiments as a tool for operon prediction. Nucleic Acids Res. 2002;30:2886–2893
  10. Westover BP, Buhler JD, Sonnenburg JL, Gordon JI. Operon prediction without a training set. Bioinformatics. 2005;21:880–888
  11. Edwards MT, Rison SCG, Stoker NG, Wernisch L. A universally applicable method of operon map prediction on minimally annotated genomes using conserved genomic context. Nucleic Acids Res. 2005;33:3253–3262
  12. Craven M, Page D, Shavlik J, Bockhorst J, Glasner J. A probabilistic learning approach to whole-genome operon prediction. In: Proceedings of the 8th international conference on intelligent systems for molecular biology (ISMB 2000). San Diego, California, USA. AAAI Press; 2000;p. 116–127
  13. Chen X, Su ZC, Xu Y, Jiang T. Computational prediction of operons in Synechococcus sp. WH8102. Genome Inform. 2004;15:211–222
  14. Dam P, Olman V, Xu Y. Improving operon prediction in E. coli. In:  Martin DC editors. Proceedings of the 4th international IEEE Computer Society Computational Systems Bioinformatics conference workshops & poster abstracts (CSB 2005 Workshops). Stanford, CA, USA, August 8–11. IEEE Computer Society; 2005;p. 69–70
  15. Jacob E, Sasikumar R, Nair KNR. A fuzzy-guided genetic algorithm for operon prediction. Bioinformatics. 2005;21:1403–1407
  16. Moreno-Hagelsieb G, Collado-Vides J. A powerful non-homology method for the prediction of operons in prokaryotes. Bioinformatics. 2002;18:329–336
  17. Bockhorst J, Craven M, Page D, Shavlik J, Glasner J. A Bayesian network approach to operon prediction. Bioinformatics. 2003;19:1227–1235
  18. Wang LS, Trawick JD, Yamamoto R, Zamudio C. Genome-wide operon prediction in Staphylococcus aureus. Nucleic Acids Res. 2004;32:3689–3702
  19. Ogata H, Fujibuchi W, Goto S, Kanehisa M. Aheuristic graph comparison algorithm and its application to detect functionally related enzyme clusters. Nucleic Acids Res. 2000;28:4021–4028
  20. Price MN, Huang KH, Alm EJ, Arkin AP. A novel method for accurate operon predictions in all sequenced prokaryotes. Nucleic Acids Res. 2005;33:880–892
  21. Stover KC, Pham XQ, Erwin AL, Mizoguchi SD, Warrener P, Hickey MJ, et al. Complete genome sequence of Pseudomonas aeruginosa PAO1: an opportunistic pathogen. Nature. 2000;406:959–964
  22. Salgado H, Gama-Castro S, Peralta-Gil M, Diaz-Peredo E, Sanchez-Solano F, Santos-Zavaleta A, et al. RegulonDB (Version 5.0): Escherichia coli K12 transcriptional regulatory network, operon organization, and growth conditions. Nucleic Acids Res. 2006;34:394–397
  23. Okuda S, Katayama T, Kawashima S, Goto S, Kanehisa M. ODB: a database of operons accumulating known operons across multiple genomes. Nucleic Acids Res. 2006;34:D358–D362
  24. Keseler IM, Collado-Vides J, Gama-Castro S, Ingraham J, Paley S, Paulsen IT, et al. EcoCyc: a comprehensive database resource for Escherichia coli. Nucleic Acids Res. 2005;33:334–337
  25. Barrett T, Suzek TO, Troup DB, Wilhite SE, Ngau WC, Ledoux P, et al. NCBI GEO: mining millions of expression profiles-database and tools. Nucleic Acids Res. 2005;33:562–566
  26. Shannon CE. A mathematical theory of communication. Bell Syst Techn J. 1948;27:379–423

PII: S0933-3657(07)00096-6

doi: 10.1016/j.artmed.2007.07.010

Artificial Intelligence in Medicine
Volume 41, Issue 2 , Pages 151-159 , October 2007