Skip to main content
SpringerLink
Log in

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

  • Conference paper
Applications of Evolutionary Computing (EvoWorkshops 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3907))

Included in the following conference series:

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)

    MATH  Google Scholar 

  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. 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. 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. 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)

    Article  Google Scholar 

  6. Kent, W.J.: BLAT–the BLAST-like alignment tool. Genome Research 12(4), 656–664 (2002)

    MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  9. Li, F., Stormo, G.D.: Selection of optimal DNA oligos for gene expression arrays. Bioinformatics 17, 1067–1076 (2001)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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. Tomiuk, S., Hofmann, K.: Microarray probe selection strategies. Briefings in Bioinformatics 2(4), 329–340 (2001)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  18. Wang, X., Seed, B.: Selection of oligonucleotide probes for protein coding sequences. Bioinformatics 19(7), 796–802 (2003)

    Article  Google Scholar 

  19. Zuker, M.: Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Research 31(13), 3406–3415 (2003)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Biointelligence Laboratory, School of Computer Science and Engineering, Seoul National University, Seoul, 151-742, Korea

    Soo-Yong Shin, In-Hee Lee & Byoung-Tak Zhang

Authors
  1. Soo-Yong Shin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. In-Hee Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Byoung-Tak Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Johannes Gutenberg University, Mainz, Germany

    Franz Rothlauf

  2. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  3. Dipartimento di Ingegneria dell’Informazione, Università di Parma,  

    Stefano Cagnoni

  4. Centre of Informatics and Systems of the University of Coimbra,  

    Ernesto Costa

  5. Dept. LCC, Universidad de Málaga, Spain

    Carlos Cotta

  6. Institute of Computer Science, University of Bremen, 28359, Bremen, Germany

    Rolf Drechsler

  7. INRIA Saclay - Ile-de-France, Parc Orsay Université, 4, rue Jacques Monod, 91893, ORSAY Cedex, France

    Evelyne Lutton

  8. CISUC, Department of Informatics Engineering, University of Coimbra, Polo II of the University of Coimbra, 3030, Coimbra, Portugal

    Penousal Machado

  9. Dartmouth College, Lebanon, NH, USA

    Jason H. Moore

  10. Universidade de A Coruña, CP 15071, A Coruña, Spain

    Juan Romero

  11. School of Computing Sciences, UEA Norwich, University of East Anglia, NR4 7TJ, Norwich, UK

    George D. Smith

  12. Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy

    Giovanni Squillero

  13. Kyushu University, Japan

    Hideyuki Takagi

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Shin, SY., Lee, IH., Zhang, BT. (2006). Microarray Probe Design Using ε-Multi-Objective Evolutionary Algorithms with Thermodynamic Criteria. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2006. Lecture Notes in Computer Science, vol 3907. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732242_17

Download citation

  • DOI: https://doi.org/10.1007/11732242_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33237-4

  • Online ISBN: 978-3-540-33238-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation