Microarray Probe Design Using ε-Multi-Objective Evolutionary Algorithms with Thermodynamic Criteria

  • Soo-Yong Shin
  • In-Hee Lee
  • Byoung-Tak Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)


As DNA microarrays have been widely used for gene expression profiling and other fields, the importance of reliable probe design for microarray has been highlighted. First, the probe design for DNA microarray was formulated as a constrained multi-objective optimization task by investigating the characteristics of probe design. Then the probe set for human paillomavrius (HPV) was found using ε-multi-objective evolutionary algorithm with thermodynamic fitness calculation. The evolutionary optimization of probe set showed better results than the commercial microarray probe set made by Biomedlab Co. Korea.


Probe Design Sequence Similarity Search Thermodynamic Criterion Archive Member Single Objective Case 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)MATHGoogle Scholar
  2. 2.
    Deb, K., Mohan, M., Mishra, S.: A fast multi-objective evolutionary algorithm for finding well-spread pareto-optimal solutions. In: KanGAL Report 2003002, Kanpur Genetic Algorithm Laboratory, Indian Institute of Technology Kanpur (2003)Google Scholar
  3. 3.
    Deb, K., Mohan, M., Mishra, S.: Towards a quick computation of well-spread Pareto-optimal solutions. In: Proceedings of the Second International Conference on Evolutionary Multi-Criterion Optimization, pp. 222–236 (2003)Google Scholar
  4. 4.
    Flikka, K., Yadetie, F., Laegreid, A., Jonassen, I.: Xhm: A system for detection of potential cross hybridizations in dna microarrays. BMC Bioinformatics 5(117) (2004)Google Scholar
  5. 5.
    Gordon, P.M.K., Sensen, C.W.: Osprey: a comprehensive tool employing novel methods for design of oligonecleotides for dna sequencing and microarrays. Nucleic Acid Research, 32(17), e133 (2004)CrossRefGoogle Scholar
  6. 6.
    Kent, W.J.: BLAT–the BLAST-like alignment tool. Genome Research 12(4), 656–664 (2002)MathSciNetGoogle Scholar
  7. 7.
    Laumanns, M., Thiele, L., Deb, K., Zitzler, E.: Combining convergence and diversity in evolutionary multi-objective optimizatin. Evolutionary Computation 10(3), 263–282 (2002)CrossRefGoogle Scholar
  8. 8.
    Lee, I.-H., Kim, S., Zhang, B.-T.: Multi-objective evolutionary probe design based on thermodynamic criteria for HPV detection. In: Zhang, C., Guesgen, H.W., Yeap, W.-K. (eds.) PRICAI 2004. LNCS (LNAI), vol. 3157, pp. 742–750. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Li, F., Stormo, G.D.: Selection of optimal DNA oligos for gene expression arrays. Bioinformatics 17, 1067–1076 (2001)CrossRefGoogle Scholar
  10. 10.
    Rouillard, J.-M., Zuker, M., Gulari, E.: OligoArray 2. 0: design of oligonucleotide probes for DNA microarrays using a thermodynamic approach. Nucleic Acids Research 31(12), 3057–3062 (2003)Google Scholar
  11. 11.
    SantaLucia Jr., J.: A unified view of polymer, dumbbell, and oligonucleotide DNA nearest-neighbor thermodynamics. Proceedings of the National Academy of Science of the United States of America 95, 1460–1465 (1998)CrossRefGoogle Scholar
  12. 12.
    Shin, S.-Y.: Multi-Objective Evolutionary Optimization of DNA Sequences for Molecular Computing. PhD thesis, School of Computer Science and Engineering. Seoul National University, Seoul, Korea (2005)Google Scholar
  13. 13.
    Shin, S.-Y., Jang, H.-Y., Tak, M.-H., Zhang, B.-T.: Simulation of DNA hybridization chain reaction based on thermodynamics and artificial chemistry. In: Preliminary Proceedings of 9th International Meeting on DNA Based Computer, p. 451 (2004)Google Scholar
  14. 14.
    Shin, S.-Y., Lee, I.-H., Kim, D., Zhang, B.-T.: Multi-objective evolutionary optimization of DNA sequences for reliable DNA computing. IEEE Transactions on Evolutionary Computation 9(2), 143–158 (2005)CrossRefGoogle Scholar
  15. 15.
    Tobler, J.B., Molla, M.N., Nuwaysir, E.F., Green, R.D., Shavlik, J.W.: Evaluating machine learning approaches for aiding probe selection for gene-expression arrays. Bioinformatics 18, 164–171 (2002)Google Scholar
  16. 16.
    Tomiuk, S., Hofmann, K.: Microarray probe selection strategies. Briefings in Bioinformatics 2(4), 329–340 (2001)CrossRefGoogle Scholar
  17. 17.
    Walboomers, J.M.M., Jacobs, M.V., Manos, M.M., Bosch, F.X., Kummer, J.A., Shah, K.V., Snijders, P.J.F., Peto, J., Meijer, C.J.L.M., Munoz, N.: Human papillomavirus is a neccsary cause of invasive cervical cancer worldwide. Journal of Pathology 189(1), 12–19 (1999)CrossRefGoogle Scholar
  18. 18.
    Wang, X., Seed, B.: Selection of oligonucleotide probes for protein coding sequences. Bioinformatics 19(7), 796–802 (2003)CrossRefGoogle Scholar
  19. 19.
    Zuker, M.: Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Research 31(13), 3406–3415 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Soo-Yong Shin
    • 1
  • In-Hee Lee
    • 1
  • Byoung-Tak Zhang
    • 1
  1. 1.Biointelligence Laboratory, School of Computer Science and EngineeringSeoul National UniversitySeoulKorea

Personalised recommendations