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Computational Intelligence Algorithms and DNA Microarrays

Chapter
Part of the Studies in Computational Intelligence book series (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.

Keywords

Cluster Algorithm Differential Evolution Training Algorithm Gene Subset Dimension Reduction Technique 
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.

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References

  1. 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) 1370–1386CrossRefGoogle Scholar
  2. 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) 86–112CrossRefGoogle Scholar
  3. 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) 631–643CrossRefGoogle Scholar
  4. 4.
    Wall, M., Rechtsteiner, A., Rocha, L.: Singular value decomposition and principal component analysis. In: A Practical Approach to Microarray Data Analysis. Kluwer (2003) 91–109Google Scholar
  5. 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) 363–394CrossRefMathSciNetGoogle Scholar
  6. 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) 314–321Google Scholar
  7. 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) 755–761CrossRefGoogle Scholar
  8. 8.
    Rumelhart, D., Hinton, G., Williams, R.: Learning internal representations by error propagation. MIT Press Cambridge, MA, USA (1986)Google Scholar
  9. 9.
    Gill, P., Murray, W., Wright, M.: Practical optimization. London: Academic Press, (1981)Google Scholar
  10. 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) 586–591Google Scholar
  11. 11.
    Sutton, R., Whitehead, S.: Online learning with random representations. Proceedings of the Tenth International Conference on Machine Learning (1993) 314–321Google Scholar
  12. 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. 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. 14.
    Bäck, T., Schwefel, H.: An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation 1(1) (1993) 1–23CrossRefGoogle Scholar
  15. 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) 341–359CrossRefMathSciNetGoogle Scholar
  16. 16.
    Storn, R., Price, K.: Minimizing the real functions of the icec’96 contest by differential evolution. In: IEEE Conference on Evolutionary Computation. (1996) 842–844Google Scholar
  17. 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) 519–525CrossRefGoogle Scholar
  18. 18.
    Ilonen, J., Kamarainen, J.K., Lampinen, J.: Differential evolution training algorithm for feed forward neural networks. Neural Processing Letters 17(1) (2003) 93–105CrossRefGoogle Scholar
  19. 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) 2007–2013Google Scholar
  20. 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) 910–915Google Scholar
  21. 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) 2023–2029Google Scholar
  22. 22.
    Plagianakos, V.P., Vrahatis, M.N.: Parallel evolutionary training algorithms for ‘hardware-friendly’ neural networks. Natural Computing 1 (2002) 307–322CrossRefMathSciNetGoogle Scholar
  23. 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) 1847–1854CrossRefGoogle Scholar
  24. 24.
    John, G., Kohavi, R., Pfleger, K.: Irrelevant features and the subset selection problem. In: International Conference on Machine Learning. (1994) 121–129Google Scholar
  25. 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) 61–72Google Scholar
  26. 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) 94–105Google Scholar
  27. 27.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer-Verlag (2001)Google Scholar
  28. 28.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: Advances in Knowledge Discovery and Data Mining. MIT Press (1996)Google Scholar
  29. 29.
    Aldenderfer, M., Blashfield, R.: Cluster Analysis. Volume 44 of Quantitative Applications in the Social Sciences. SAGE Publications, London (1984)Google Scholar
  30. 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) 518–531CrossRefGoogle Scholar
  31. 31.
    Becker, R., Lago, G.: A global optimization algorithm. In: Proceedings of the 8th Allerton Conference on Circuits and Systems Theory. (1970) 3–12Google Scholar
  32. 32.
    Torn, A., Zilinskas, A.: Global Optimization. Springer-Verlag, Berlin (1989)Google Scholar
  33. 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) 6745–6750CrossRefGoogle Scholar
  34. 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) 14863–14868CrossRefGoogle Scholar
  35. 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. 36.
    Tavazoie, S., Hughes, J., Campbell, M., Cho, R., Church, G.: Systematic determination of genetic network architecture. Nature Genetics volume 22 (1999) 281–285CrossRefGoogle Scholar
  37. 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. 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) 334–339CrossRefGoogle Scholar
  39. 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) 531–537CrossRefGoogle Scholar
  40. 40.
    Jain, A., Murty, M., Flynn, P.: Data clustering: a review. ACM Computing Surveys 31(3) (1999) 264–323CrossRefGoogle Scholar
  41. 41.
    Alizadeh, A., et al.: Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling. Nature 403(6769) (2000) 503–511CrossRefGoogle Scholar
  42. 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) 9212–9217CrossRefGoogle Scholar
  43. 43.
    Xing, E., Karp, R.: Cliff: Clustering of high–dimensional microarray data via iterative feature filtering using normalized cuts. Bioinformatics Discovery Note 1 (2001) 1–9Google Scholar
  44. 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) 2907–2912CrossRefGoogle Scholar
  45. 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) 10101–10106CrossRefGoogle Scholar
  46. 46.
    Szallasi, Z., Somogyi, R.: Genetic network analysis – the millennium opening version. In: Pacific Symposium of BioComputing Tutorial. (2001)Google Scholar
  47. 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) 347–351Google Scholar
  48. 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) 375–391CrossRefMathSciNetGoogle Scholar
  49. 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) 169–194CrossRefGoogle Scholar
  50. 50.
    Boley, D.: Principal direction divisive partitioning. Data Mining and Knowledge Discovery 2(4) (1998) 325–344CrossRefGoogle Scholar
  51. 51.
    Bezdek, J.: Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers (1981)Google Scholar
  52. 52.
    Fritzke, B.: Growing cell structures a self-organizing network for unsupervised and supervised learning. Neural Netw. 7(9) (1994) 1441–1460CrossRefGoogle Scholar
  53. 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. 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) 226–231Google Scholar
  55. 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) 418–427CrossRefGoogle Scholar
  56. 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) 25–72CrossRefGoogle Scholar
  57. 57.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Computing Surveys 31(3) (1999) 264–323CrossRefGoogle Scholar
  58. 58.
    Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Addison-Wesley, Boston (2005)Google Scholar
  59. 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) 51–78Google Scholar
  60. 60.
    Hartigan, J., Wong, M.: A k-means clustering algorithm. Applied Statistics 28 (1979) 100–108CrossRefGoogle Scholar
  61. 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) 26–35Google Scholar
  62. 62.
    Singh, D., et al.: Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1 (2002) 203–209CrossRefGoogle Scholar
  63. 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) 1227–1236CrossRefGoogle Scholar
  64. 64.
    Kohonen, T.: Self–Organized Maps. Springer Verlag, New York, Berlin (1997)Google Scholar
  65. 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) 181–190CrossRefGoogle Scholar
  66. 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

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  1. 1.Institute for Mathematical SciencesImperial College LondonLondonUK
  2. 2.Computational Intelligence Laboratory, Department of Mathematics, University of Patras Artificial Intelligence Research Center (UPAIRC)University of PatrasPatrasGreece

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