Skip to main content

Computational Intelligence Algorithms and DNA Microarrays

  • Chapter

Part of the book series: Studies in Computational Intelligence ((SCI,volume 94))

Summary

In this chapter, we present Computational Intelligence algorithms, such as Neural Network algorithms, Evolutionary Algorithms, and clustering algorithms and their application to DNA microarray experimental data analysis. Additionally, dimension reduction techniques are evaluated. Our aim is to study and compare various Computational Intelligence approaches and demonstrate their applicability as well as their weaknesses and shortcomings to efficient DNA microarray data analysis.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Jiang, D., Tang, C., Zhangi, A.: Cluster analysis for gene expression data: A survey. IEEE Transactions on Knowledge and Data Engineering 16(11) (2004) 1370–1386

    Article  Google Scholar 

  2. Larranaga, P., Calvo, B., Santana, R., Bielza, C., Galdiano, J., Inza, I., Lozano, J.A., Armananzas, R., Santafe, G., Perez, A., Robles, V.: Machine learning in bioinformatics. Briefings in Bioinformatics 7(1) (2006) 86–112

    Article  Google Scholar 

  3. Statnikov, A., Aliferis, C.F., Tsamardinos, I., Hardin, D., Levy, S.: A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics 21(5) (2005) 631–643

    Article  Google Scholar 

  4. Wall, M., Rechtsteiner, A., Rocha, L.: Singular value decomposition and principal component analysis. In: A Practical Approach to Microarray Data Analysis. Kluwer (2003) 91–109

    Google Scholar 

  5. Van Mechelen, I., Bock, H.H., De Boeck, P.: Two-mode clustering methods:a structured overview. Statistical Methods in Medical Research 13(5) (2004) 363–394

    Article  MathSciNet  Google Scholar 

  6. Kung, S.Y., Mak, M.W.: A Machine Learning Approach to DNA Microarray Biclustering Analysis. In: Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, (2005) 314–321

    Google Scholar 

  7. Wang, Z., Wang, Y., Xuan, J., Dong, Y., Bakay, M., Feng, Y., Clarke, R., Hoffman, E.P.: Optimized multilayer perceptrons for molecular classification and diagnosis using genomic data. Bioinformatics 22(6) (2006) 755–761

    Article  Google Scholar 

  8. Rumelhart, D., Hinton, G., Williams, R.: Learning internal representations by error propagation. MIT Press Cambridge, MA, USA (1986)

    Google Scholar 

  9. Gill, P., Murray, W., Wright, M.: Practical optimization. London: Academic Press, (1981)

    Google Scholar 

  10. Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: The RPROP algorithm. In: Proceedings of the IEEE International Conference on Neural Networks, San Francisco, CA. (1993) 586–591

    Google Scholar 

  11. Sutton, R., Whitehead, S.: Online learning with random representations. Proceedings of the Tenth International Conference on Machine Learning (1993) 314–321

    Google Scholar 

  12. Magoulas, G., Plagianakos, V.P., Vrahatis, M.N.: Development and convergence analysis of training algorithms with local learning rate adaptation. In: IEEE International Joint Conference on Neural Networks (IJCNN’2000), 1 (2000) 21–26.

    Google Scholar 

  13. Plagianakos, V.P., Magoulas, G., Vrahatis, M.N.: Global learning rate adaptation in on-line neural network training. In: Second International ICSC Symposium on Neural Computation (NC’2000). (2000)

    Google Scholar 

  14. Bäck, T., Schwefel, H.: An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation 1(1) (1993) 1–23

    Article  Google Scholar 

  15. Storn, R., Price, K.: Differential evolution – a simple and efficient adaptive scheme for global optimization over continuous spaces. Journal of Global Optimization 11 (1997) 341–359

    Article  MathSciNet  Google Scholar 

  16. Storn, R., Price, K.: Minimizing the real functions of the icec’96 contest by differential evolution. In: IEEE Conference on Evolutionary Computation. (1996) 842–844

    Google Scholar 

  17. DiSilvestro, M., Suh, J.K.: A cross-validation of the biphasic poroviscoelastic model of articular cartilage in unconfined compression, indentation, and confined compression. Journal of Biomechanics 34 (2001) 519–525

    Article  Google Scholar 

  18. Ilonen, J., Kamarainen, J.K., Lampinen, J.: Differential evolution training algorithm for feed forward neural networks. Neural Processing Letters 17(1) (2003) 93–105

    Article  Google Scholar 

  19. Plagianakos, V.P., Vrahatis, M.N.: Neural network training with constrained integer weights. In Angeline, P., Michalewicz, Z., Schoenauer, M., Yao, X., Zalzala, A., eds.: Proceedings of the Congress of Evolutionary Computation (CEC’99). IEEE Press (1999) 2007–2013

    Google Scholar 

  20. Plagianakos, V.P., Vrahatis, M.N.: Training neural networks with 3–bit integer weights. In Banzhaf, W., Daida, J., Eiben, A., Garzon, M., Honavar, V., Jakiela, M., Smith, R., eds.: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’99). Morgan Kaufmann (1999) 910–915

    Google Scholar 

  21. Tasoulis, D.K., Pavlidis, N.G., Plagianakos, V.P., Vrahatis, M.N.: Parallel differential evolution. In: IEEE Congress on Evolutionary Computation (CEC 2004), 2 (2004) 2023–2029

    Google Scholar 

  22. Plagianakos, V.P., Vrahatis, M.N.: Parallel evolutionary training algorithms for ‘hardware-friendly’ neural networks. Natural Computing 1 (2002) 307–322

    Article  MathSciNet  Google Scholar 

  23. Tasoulis, D.K., Plagianakos, V.P., Vrahatis, M.N.: Clustering in evolutionary algorithms to efficiently compute simultaneously local and global minima. In: IEEE Congress on Evolutionary Computation. Volume 2., Edinburgh, UK (2005) 1847–1854

    Chapter  Google Scholar 

  24. John, G., Kohavi, R., Pfleger, K.: Irrelevant features and the subset selection problem. In: International Conference on Machine Learning. (1994) 121–129

    Google Scholar 

  25. Aggarwal, C., Wolf, J., Yu, P., Procopiuc, C., Park, J.: Fast algorithms for projected clustering. In: 1999 ACM SIGMOD international conference on Management of data, ACM Press (1999) 61–72

    Google Scholar 

  26. Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic subspace clustering of high dimensional data for data mining applications. In: 1998 ACM SIGMOD international conference on Management of data, ACM Press (1998) 94–105

    Google Scholar 

  27. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer-Verlag (2001)

    Google Scholar 

  28. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: Advances in Knowledge Discovery and Data Mining. MIT Press (1996)

    Google Scholar 

  29. Aldenderfer, M., Blashfield, R.: Cluster Analysis. Volume 44 of Quantitative Applications in the Social Sciences. SAGE Publications, London (1984)

    Google Scholar 

  30. Ramasubramanian, V., Paliwal, K.: Fast k-dimensional tree algorithms for nearest neighbor search with application to vector quantization encoding. IEEE Transactions on Signal Processing 40(3) (1992) 518–531

    Article  Google Scholar 

  31. Becker, R., Lago, G.: A global optimization algorithm. In: Proceedings of the 8th Allerton Conference on Circuits and Systems Theory. (1970) 3–12

    Google Scholar 

  32. Torn, A., Zilinskas, A.: Global Optimization. Springer-Verlag, Berlin (1989)

    Google Scholar 

  33. Alon, U., Barkai, N., Notterman, D., K.Gish, Ybarra, S., Mack, D., Levine, A.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide array. Proc. Natl. Acad. Sci. USA 96(12) (1999) 6745–6750

    Article  Google Scholar 

  34. Eisen, M., Spellman, P., Brown, P., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95 (1998) 14863–14868

    Article  Google Scholar 

  35. Shamir, R., Sharan, R.: Click: A clustering algorithm for gene expression analysis. In: 8th International Conference on Intelligent Systems for Molecular Biology (ISMB 00), AAAI Press (2000)

    Google Scholar 

  36. Tavazoie, S., Hughes, J., Campbell, M., Cho, R., Church, G.: Systematic determination of genetic network architecture. Nature Genetics volume 22 (1999) 281–285

    Article  Google Scholar 

  37. Tasoulis, D.K., Plagianakos, V.P., Vrahatis, M.N.: Unsupervised clustering in mRNA expression profiles. Computers in Biology and Medicine 36(10) (2006)

    Google Scholar 

  38. Wen, X., Fuhrman, S., Michaels, G., Carr, D., Smith, S., Barker, J., Somogyi, R.: Large-scale temporal gene expression mapping of cns development. Proceedings of the National Academy of Science USA 95 (1998) 334–339

    Article  Google Scholar 

  39. Golub, T., Slomin, D., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J., Coller, H., Loh, M., Downing, J., Caligiuri, M., Bloomfield, C., Lander, E.: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286 (1999) 531–537

    Article  Google Scholar 

  40. Jain, A., Murty, M., Flynn, P.: Data clustering: a review. ACM Computing Surveys 31(3) (1999) 264–323

    Article  Google Scholar 

  41. Alizadeh, A., et al.: Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling. Nature 403(6769) (2000) 503–511

    Article  Google Scholar 

  42. Perou C., Jeffrey, S., de Rijn, M.V., Rees, C., Eisen, M., Ross, D., Pergamenschikov, A., Williams, C., Zhu, S., J.C. Lee, D.L., Shalon, D., Brown, P., Botstein, D.: Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proc. Natl. Acad. Sci. USA 96 (1999) 9212–9217

    Article  Google Scholar 

  43. Xing, E., Karp, R.: Cliff: Clustering of high–dimensional microarray data via iterative feature filtering using normalized cuts. Bioinformatics Discovery Note 1 (2001) 1–9

    Google Scholar 

  44. Tamayo, P., Slonim, D., Mesirov, Q., Zhu, J., Kitareewan, S., Dmitrovsky, E., Lander, E., Golub, T.: Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proc. Natl. Acad. Sci. USA 96 (1999) 2907–2912

    Article  Google Scholar 

  45. Alter, O., Brown, P., Bostein, D.: Singular value decomposition for genome-wide expression data processing and modeling. Proc. Natl. Acad. Sci. USA 97(18) (2000) 10101–10106

    Article  Google Scholar 

  46. Szallasi, Z., Somogyi, R.: Genetic network analysis – the millennium opening version. In: Pacific Symposium of BioComputing Tutorial. (2001)

    Google Scholar 

  47. Tasoulis, D.K., Vrahatis, M.N.: Unsupervised distributed clustering. In: Proceedings of the IASTED International Conference on Parallel and Distributed Computing and Networks, Innsbruck, Austria (2004) 347–351

    Google Scholar 

  48. Vrahatis, M.N., Boutsinas, B., Alevizos, P., Pavlides, G.: The new k-windows algorithm for improving the k-means clustering algorithm. Journal of Complexity 18 (2002) 375–391

    Article  MathSciNet  Google Scholar 

  49. Sander, J., Ester, M., Kriegel, H.P., Xu, X.: Density-based clustering in spatial databases: The algorithm gdbscan and its applications. Data Mining and Knowledge Discovery 2(2) (1998) 169–194

    Article  Google Scholar 

  50. Boley, D.: Principal direction divisive partitioning. Data Mining and Knowledge Discovery 2(4) (1998) 325–344

    Article  Google Scholar 

  51. Bezdek, J.: Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers (1981)

    Google Scholar 

  52. Fritzke, B.: Growing cell structures a self-organizing network for unsupervised and supervised learning. Neural Netw. 7(9) (1994) 1441–1460

    Article  Google Scholar 

  53. Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: Optics: Ordering points to identify the clustering structure. In: Proceedings of ACM-SIGMOD International Conference on Management of Data. (1999)

    Google Scholar 

  54. Ester, M., Kriegel, H., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd Int. Conf. on Knowledge Discovery and Data Mining. (1996) 226–231

    Google Scholar 

  55. Procopiuc, C., Jones, M., Agarwal, P., Murali, T.: A Monte Carlo algorithm for fast projective clustering. In: Proc. 2002 ACM SIGMOD, New York, NY, USA, ACM Press (2002) 418–427

    Chapter  Google Scholar 

  56. Berkhin, P.: A survey of clustering data mining techniques. In Kogan, J., Nicholas, C., Teboulle, M., eds.: Grouping Multidimensional Data: Recent Advances in Clustering. Springer, Berlin (2006) 25–72

    Chapter  Google Scholar 

  57. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Computing Surveys 31(3) (1999) 264–323

    Article  Google Scholar 

  58. Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Addison-Wesley, Boston (2005)

    Google Scholar 

  59. Tasoulis, D.K., Vrahatis, M.N.: Novel approaches to unsupervised clustering through the k-windows algorithm. In Sirmakessis, S., ed.: Knowledge Mining. Volume 185 of Studies in Fuzziness and Soft Computing. Springer-Verlag (2005) 51–78

    Google Scholar 

  60. Hartigan, J., Wong, M.: A k-means clustering algorithm. Applied Statistics 28 (1979) 100–108

    Article  Google Scholar 

  61. Zeimpekis, D., Gallopoulos, E.: PDDP(l): Towards a Flexing Principal Direction Divisive Partitioning Clustering Algorithms. In Boley, D., Dhillon, I., Ghosh, J., Kogan, J., eds.: Proc. IEEE ICDM ’03 Workshop on Clustering Large Data Sets, Melbourne, Florida (2003) 26–35

    Google Scholar 

  62. Singh, D., et al.: Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1 (2002) 203–209

    Article  Google Scholar 

  63. Thomas, J., Olson, J., Tapscott, S., Zhao, L.: An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles. Genome Research 11 (2001) 1227–1236

    Article  Google Scholar 

  64. Kohonen, T.: Self–Organized Maps. Springer Verlag, New York, Berlin (1997)

    Google Scholar 

  65. Ye, J., Li, T., Xiong, T., Janardan, R.: Using uncorrelated discriminant analysis for tissue classification with gene expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics 1(4) (2004) 181–190

    Article  Google Scholar 

  66. Plagianakos, V.P., Tasoulis, D.K., Vrahatis, M.N.: Hybrid dimension reduction approach for gene expression data classification. In: International Joint Conference on Neural Networks 2005, Post-Conference Workshop on Computational Intelligence Approaches for the Analysis of Bioinformatics Data. (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Tasoulis, D.K., Plagianakos, V.P., Vrahatis, M.N. (2008). Computational Intelligence Algorithms and DNA Microarrays. In: Kelemen, A., Abraham, A., Chen, Y. (eds) Computational Intelligence in Bioinformatics. Studies in Computational Intelligence, vol 94. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76803-6_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-76803-6_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76802-9

  • Online ISBN: 978-3-540-76803-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics