Artificial Intelligence in Medicine
Volume 41, Issue 2 , Pages 161-175 , October 2007

A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue

  • Zhenyu Chen

      Affiliations

    • Institute of Policy & Management, Chinese Academy of Sciences, Beijing 100080, China
    • Graduate University of Chinese Academy of Sciences, Beijing 100039, China
  • ,
  • Jianping Li

      Affiliations

    • Institute of Policy & Management, Chinese Academy of Sciences, Beijing 100080, China
    • Corresponding Author InformationCorresponding author. Tel.: +86 10 6263 4957; fax: +86 10 6254 2629.
  • ,
  • Liwei Wei

      Affiliations

    • Institute of Policy & Management, Chinese Academy of Sciences, Beijing 100080, China
    • Graduate University of Chinese Academy of Sciences, Beijing 100039, China

Received 30 November 2006 ,Revised 31 July 2007 ,Accepted 31 July 2007.

References 

  1. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasengeek M, Mesirov JP, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999;286:531–537
  2. van’t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart A, Mao M, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415:530–536
  3. Matthias EF, Anthony R, Nikola K. Evolving connectionist systems for knowledge discovery from gene expression data of cancer tissue. Artif Intell Med. 2003;28:165–189
  4. Yeoh E, Ross ME, Shurtleff SA. Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer cell. 2002;1:133–143
  5. Slonim K. From patterns to pathways: gene expression data analysis comes of age. Nat Genet. 2002;32:502–508
  6. Tung WL, Quek C. GenSo-FDSS: a neural-fuzzy decision support system for pediatric ALL cancer subtype identification using gene expression data. Artif Intell Med. 2005;33:61–88
  7. Sacchi L, Bellazy R, Larizza C, Magni P, Curk T, Petrovic U, et al. TA-clustering: cluster analysis of gene expression profiles through temporal abstractions. Int J Med Inform. 2005;74:505–517
  8. Hautaniemi S, Yli-Harja O, Astola J, Kauraniemi P, Kallioniemi A, Wolf M, et al. Analysis and visualization of gene expression microarray data in human cancer using self-organizing maps. Mach Learn. 2003;52:45–66
  9. Tomida S, Hanai T, Honda H, Kobayashi T. Analysis of expression profile using fuzzy adaptive resonance theory. Bioinformatics. 2002;18:1073–1083
  10. Takahashi H, Kobayashi T, Honda H. Construction of robust prognostic predictors by using projective adaptive resonance theory as a gene filtering method. Bioinformatics. 2005;2:179–186
  11. Li L, Weinberg CR, Darden TA, Pedersen LG. Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics. 2001;17:1131–1142
  12. Xu Y, Selaru FM, Yin J, Zou TT, Shustova V, Mori Y, et al. Artificial neural networks and gene filtering distinguish between global gene expression profiles of Barrett's esophagus and esophageal cancer. Cancer Res. 2002;62:3493–3497
  13. Ando T, Suguro M, Kobayashi T, Seto M, Honda H. Selection of causal gene sets for lymphoma prognostication from expression profiling and construction of prognostic fuzzy neural network models. J Biosci Bioeng. 2003;96:161–167
  14. Takahashi H, Masuda K, Ando T, Kobayashi T, Honda H. Prognostic predictor with multiple fuzzy neural models using expression profiles from DNA microarray for metastases of breast cancer. J Biosci Bioeng. 2004;98:193–199
  15. Roth V, Lange T. Bayesian class discovery in microarray dataset. IEEE Trans Biomed Eng. 2004;51:707–718
  16. Sevon P, Toivonen H, Ollikainen V. TreeDT: tree pattern mining for gene mapping. IEEE/ACM Trans Comput Biol Bioinform. 2006;3:174–185
  17. Mao Y, Zhou X, Pi D, Sun Y, Wong S. Multiclass cancer classification by using fuzzy support vector machine and binary decision tree with gene selection. J Biomed Biotechnol. 2005;2:160–171
  18. Wu W, Liu X, Xu M, Peng J, Rudy S. A hybrid SOM-SVM method for analyzing zebra fish gene expression. In:  Kittler J,  Petrou M,  Nixon M editor. Proceedings of the 17th international conference on pattern recognition. Los Alamitos, California, United States: IEEE Computer Society; 2004;p. 323–326
  19. Isabella G, Jason W, Stephen B, Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn. 2002;46:389–422
  20. Dettling M, Bühlmann P. Boosting for tumor classification with gene expression data. Bioinformatics. 2003;19:1061–1069
  21. Monti S, Tamayo P, Mesirov J, Golub T. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach Learn. 2003;52:91–118
  22. Hong JH, Cho SB. The classification of cancer based on DNA microarray data that uses diverse ensemble genetic programming. Artif Intell Med. 2006;36:43–58
  23. Vapnik V. The nature of statistic learning theory. New York: Springer; 1995;
  24. Paul TK, Iba H. Selection of the most useful subset of genes for gene expression-based classification. In:  Paul TK editors. Proceedings of the 2004 congress on evolutionary computation. IEEE, New York, United States. 2004;p. 2076–2083
  25. Huang CL, Wei CJ. GA-based feature selection and feature optimization for support vector machine. Expert Syst Appl. 2006;31:231–240
  26. Blum AL, Langley P. Selection of relevant features and examples in machine learning. Artif Intell. 1997;97:245–271
  27. Zhu ZX, Ong YS, Dash M. Wrapper-filter feature selection algorithm using a memetic framework. IEEE Trans Syst Man Cybern B Cybern. 2007;37:70–76
  28. Yang J, Honavar V. Feature subset selection using a genetic algorithm, feature extraction, construction and subset selection: a data mining perspective. New York: Kluwer; 1998;
  29. Inza I, Larranaga P, Blanco R, Cerrolaza AJ. Filter versus wrapper gene selection approaches in DNA microarray domains. Artif Intell Med. 2004;31:91–103
  30. Su Y, Murali TM, Pavlovic V, Schaffer M, Kasif S. RankGene: identification of diagnostic genes based on expression data. Bioinformatics. 2003;19:1578–1579
  31. Kohavi R, John GH. Wrappers for feature subset selection. Artif Intell. 1997;97:273–324
  32. Hardin D, Tsamardinos I, Aliferis: CF. A theoretical characterization of linear SVM-based feature selection. In:  Russell G,  Dale S editor. Proceedings of the 21st international conference on machine learning. New York: ACM; 2004;p. 377–384
  33. Mao KZ. Feature subset selection for support vector machines though discriminate function pruning analysis. IEEE Trans Syst Man Cybern B Cybern. 2004;34:60–67
  34. Cao L, Seng CK, Gu Q, Lee: HP. Saliency analysis of support vector machines for gene selection in tissue classification. Neural Comput Appl. 2003;11:244–249
  35. Duan K, Rajapakse JC, Wang H, Azuaje F. Multiple SVM-RFE for gene selection in cancer classification with expression data. IEEE Trans Nanobiosci. 2005;4:228–234
  36. Gregory P, Pablo T. Microarray data mining: facing the challenges. ACM SIGKDD Explor Newslett. 2003;5:1–5
  37. Roy A. On connectionism, rule extraction, and brain-like learning. IEEE Trans Fuzzy Syst. 2000;8:222–227
  38. Gupta A, Park S, Lam: SM. Generalized analytic rule extraction for feedforward neural networks. IEEE Trans Knowl Data Eng. 1999;11:985–991
  39. Kasabov NK. On-line learning, reasoning, rule extraction and aggregation in locally optimized evolving fuzzy neural networks. Neurocomputing. 2001;41:25–45
  40. Taha IA, Ghosh J. Symbolic interpretation of artificial neural networks. IEEE Trans Knowl Data Eng. 1999;11:448–463
  41. H. Nunez, C. Angulo, A. Catala. Rule extraction from support vector machines. (2002) [online] http://www.dice.ucl.ac.be/esann/proceedings/papers.php?ann=2002#ES2002-51. (Accessed: July 19, 2007).
  42. Fung G, Sandilya S, Rao R. Rule extraction from linear support vector machines. In:  Grossman RL,  Bayardo R,  Bennett K,  Vaidya J editor. Proceedings of the 11th ACM SIGKDD international conference on knowledge discovery and data mining. New York: ACM; 2005;p. 32–40
  43. Fu XJ, Ong CJ, Keerthi S, Hung GG, Goh LP. Extracting the knowledge embedded in support vector machines. In:  Grossman RL,  Bayardo R,  Bennett K,  Vaiya J editor. Proceedings of 2004 IEEE international joint conference on neural networks. IEEE, New York. 2004;p. 291–296
  44. He J, Hu HJ, Harrison R, Tai PC, Pan Y. Rule generation for protein secondary structure prediction with support vector machines and decision tree. IEEE Trans Nanobiosci. 2006;5:46–53
  45. Barakat N, Diederich J. Eclectic rule extraction from support vector machines. Int J Comput Intell. 2005;2:59–62
  46. Micchelli CA, Pontil M. Learning the kernel function via regularization. J Mach Learn Res. 2005;6:1099–1125
  47. Lanckrient GRG, Cristianini N, Bartlett P, El Ghaoui L, Jordan MI. Learning the kernel matrix with semidefinite programming. J Mach Learn Res. 2004;5:27–72
  48. Bach FR, Lanckrient GRG, Jordan MI. Multiple kernel learning, conic duality and the SMO algorithm. In:  Russell G,  Dale S editor. Proceedings of the 21st international conference on machine learning. New York: ACM; 2004;p. 41–48
  49. Sonnenburg S, Ratsch G, Schafer C. Learning interpretable SVMs for biological sequence classification. In:  Miyano S,  Mesirov J,  Kasif S,  Pevzner P,  Waterman M editor. Proceedings of the 9th annual international conference on research in computational molecular biology. Berlin: Springer; 2005;p. 389–407
  50. Gunn SR, Kandola JS. Structural modeling with sparse kernels. Mach Learn. 2002;48:137–163
  51. Chapelle O, Vapnik V, Bousquet O, Mukherfee S. Choosing multiple parameters for support vector machines. Mach Learn. 2002;46:131–159
  52. Friedrichs F, Iqel C. Evolutionary tuning of multiple SVM parameters. Neurocomputing. 2005;64:107–117
  53. Duan K, Keerthi SS, Poo AN. Evaluation of simple performance measures for tuning SVM hyperparameters. Neurocomputing. 2003;51:41–59
  54. Keerthi SS. Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms. IEEE Trans Neural Netw. 2002;13:1225–1229
  55. Demiriz A, Bennett KP, Shawe-Taylor J. Linear programming boosting via column generation. Mach Learn. 2002;46:225–254
  56. Wu Q, Zhou DX. SVM soft margin classifiers: linear programming versus quadratic programming. Neural Comput. 2005;17:1160–1187
  57. Ikeda K, Murata N. Geometrical properties of Nu support vector machines with different norms. Neural Comput. 2005;17:2508–2529
  58. Bi JB, Fung G, Dundar M, Rao B. Semi-supervised mixture of kernels via LPBoost methods. In:  Han J,  Wah BW,  Raghavan V,  Wu X,  Rastogi R editor. Proceedings of the 5th IEEE international conference on data mining. Los Alamitos, California, United States: IEEE Computer Society; 2005;p. 569–572
  59. Wang H, Huang D. Regulation probability method for gene selection. Pattern Recogn Lett. 2006;27:116–122
  60. Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, Mack D, et al. Broad patterns of gene expression revealed by clustering of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci USA. 1999;96:6745–6750
  61. Ruiz R, Riquelme JC, Aguilar-Ruiz JS. Incremental wrapper-based gene selection from microarray data for cancer classification. Pattern Recogn. 2006;39:2383–2392
  62. Li J, Wong L. Identifying good diagnostic gene groups from gene expression profiles using the concept of emerging patterns. Bioinformatics. 2002;18:725–734
  63. Tan AH, Pan H. Predictive neural networks for gene expression data analysis. Neural Netw. 2005;18:297–306

 This research has been partially supported by a grant from National Natural Science Foundation of China (#70531040), and 973 Project (#2004CB720103), Ministry of Science and Technology, China.

PII: S0933-3657(07)00097-8

doi: 10.1016/j.artmed.2007.07.008

Artificial Intelligence in Medicine
Volume 41, Issue 2 , Pages 161-175 , October 2007