Computational intelligence algorithms and DNA microarrays

Studies in Computational Intelligence

Volumn 94, Issue , 2008, Pages 1-31

  Tasoulis, D K ^a   Plagianakos, V P ^b   Vrahatis, M N ^b  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY OF PATRAS   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 38049019133     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-540-76803-6_1     Document Type: Article

References (66)

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
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
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
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
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
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
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
8. Rumelhart, D., Hinton, G., Williams, R.: Learning internal representations by error propagation. MIT Press Cambridge, MA, USA (1986)
9. Gill, P., Murray, W., Wright, M.: Practical optimization. London: Academic Press, (1981)
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
11. Sutton, R., Whitehead, S.: Online learning with random representations. Proceedings of the Tenth International Conference on Machine Learning (1993) 314-321
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.
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)
14. Bäck, T., Schwefel, H.: An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation 1(1) (1993) 1-23
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
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
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
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
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
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
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
22. Plagianakos, V.P., Vrahatis, M.N.: Parallel evolutionary training algorithms for 'hardware-friendly' neural networks. Natural Computing 1 (2002) 307-322
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
24. John, G., Kohavi, R., Pfleger, K.: Irrelevant features and the subset selection problem. In: International Conference on Machine Learning. (1994) 121-129
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
26. Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic subspace clustering of high dimensional data for data mining applications. In: 1998 ACMSIGMOD international conference on Management of data, ACM Press (1998) 94-105
27. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer-Verlag (2001)
28. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: Advances in Knowledge Discovery and Data Mining. MIT Press (1996)
29. Aldenderfer, M., Blashfield, R.: Cluster Analysis. Volume 44 of Quantitative Applications in the Social Sciences. SAGE Publications, London (1984)
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
31. Becker, R., Lago, G.: A global optimization algorithm. In: Proceedings of the 8th Allerton Conference on Circuits and Systems Theory. (1970) 3-12
32. Torn, A., Zilinskas, A.: Global Optimization. Springer-Verlag, Berlin (1989)
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
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
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)
36. Tavazoie, S., Hughes, J., Campbell, M., Cho, R., Church, G.: Systematic determination of genetic network architecture. Nature Genetics volume 22 (1999) 281-285
37. Tasoulis, D.K., Plagianakos, V.P., Vrahatis, M.N.: Unsupervised clustering in mRNA expression profiles. Computers in Biology and Medicine 36(10) (2006)
38. Wen, X., Fuhrman, S., Michaels, G., Carr, D., Smith, S., Barker, J., Somogyi, R.: Largescale temporal gene expression mapping of cns development. Proceedings of the National Academy of Science USA 95 (1998) 334-339
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
40. Jain, A., MArty, M., Flynn, P.: Data clustering: a review. ACM Computing Surveys 31(3) (1999) 264-323
41. Alizadeh, A., et al.: Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling. Nature 403(6769) (2000) 503-511
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
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
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
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
46. Szallasi, Z., Somogyi, R.: Genetic network analysis - the millennium opening version. In: Pacific Symposium of BioComputing Tutorial. (2001)
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
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
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
50. Boley, D.: Principal direction divisive partitioning. Data Mining and Knowledge Discovery 2(4) (1998) 325-344
51. Bezdek, J.: Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers (1981)
52. Fritzke, B.: Growing cell structures a self-organizing network for unsupervised and supervised learning. Neural Netw. 7(9) (1994) 1441-1460
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)
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
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
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
57. Jain, A.K.,AMurty, M.N., Flynn, P.J.: Data clustering: a review. ACM Computing Surveys 31(3) (1999) 264-323
58. Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Addison-Wesley, Boston (2005)
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
60. Hartigan, J., Wong, M.: A k-means clustering algorithm. Applied Statistics 28 (1979) 100-108
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
62. Singh, D., et al.: Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1 (2002) 203-209
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
64. Kohonen, T.: Self-Organized Maps. Springer Verlag, New York, Berlin (1997)
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
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)