Efficient Gene Selection with Rough Sets from Gene Expression Data

1. Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., Lander, E.S.: Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science 286, 531–537 (1999)

   Article  Google Scholar 

2. Wang, L.P., Feng, C., Xie, X.: Accurate Cancer Classification Using Expressions of Very Few Genes. IEEE/ACM Transactions on Computational Biology and Bioinformatics 4, 40–53 (2007)

   Article  Google Scholar 

3. Au, A., Chan, K.C.C., Wong, A.K.C., Wang, Y.: Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2, 83–101 (2005)

   Article  Google Scholar 

4. Smet, F.D., Pochet, N.L.M.M., Engelen, K., Gorp, T.V., Hummelen, P.V., Marchal, K., Amant, F., Timmerman, D., Moor, B.D., Vergote, I.: Predicting the Clinical Behavior of Ovarian Cancer from Gene Expression Profiles. International Journal of Gynecological Cancer 16, 147–151 (2006)

   Article  Google Scholar 

5. Wang, Y., Tetko, I.V., Hall, M.A., Frank, E., Facius, A., Mayer, K.F.X., Mewes, H.W.: Gene Selection from Microarray Data for Cancer Classification-A Machine Learning Approach. Computational Biology and Chemistry 29, 37–46 (2005)

   Article  MATH  Google Scholar 

6. Ding, C.: Analysis of Gene Expression Profiles: Class Discovery and Leaf Ordering. In: 6th Annual Conference on Research in Computational Molecular Biology, pp. 127–136. ACM Press, New York (2002)

   Google Scholar 

7. Pawlak, Z.: Rough Set- Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dorderecht (1991)

   Google Scholar 

8. Wang, J., Waog, J.: Reduction Algorithms Based on Discernibly Matrix: The Ordered Attributes Method. Journal of Computer Science And Technology 16, 489–504 (2002)

   Article  Google Scholar 

9. Miao, D.Q., Hu, G.R.: A Heuristic Algorithm for Reduction of Knowledge. Journal of Computer Research and Development 36, 681–684 (1999)

   Google Scholar 

10. Furey, T.S., Cristianini, N., Duffy, N., Bednarski, D.W., Schummer, M., Haussler, D.: Support Vector Machine Classification and Validation of Cancer Tissue Samples Using Microarray Expression Data. Bioinformatics 16, 906–914 (2000)

    Article  Google Scholar 

11. Valdes, J.J., Barton, A.J.: Gene Discovery in Leukemia Revisited: A Computational Intelligence Perspective. In: Orchard, B., Yang, C., Ali, M. (eds.) IEA/AIE 2004. LNCS (LNAI), vol. 3029, pp. 118–127. Springer, Heidelberg (2004)

    Google Scholar 

12. Ding, C., Peng, H.C.: Minimum Redundancy Feature Selection from Microarray Gene Expression Data. Journal of Bioinformatics and Computational Biology 3, 185–205 (2003)

    Article  Google Scholar 

13. Ben-Dor, A., Bruhn, L., Friedman, N., Nachman, I., Schummer, M., Yakhini, Z.: Tissue Classification with Gene Expression Profiles. In: 4th Annual International Conference on Computational Molecular Biology (RECOMB), pp. 54–64. Universal Academy Press, Tokyo (2000)

    Chapter  Google Scholar 

14. Tseng, V.S., Kao, C.P.: Efficiently Mining Gene Expression Data via a Novel Parameterless Clustering Method. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2, 355–365 (2005)

    Article  Google Scholar 

15. Mitra, S., Hayashi, Y.: Bioinformatics with Soft Computing. IEEE Transactions on Systems, Man and Cybernetics-Part C: Applications and Reviews 36, 616–635 (2006)

    Article  Google Scholar 

16. Fayyad, U.M., Irani, K.B.: Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In: Proceedings of the 13th International Joint Conference of Artificial Intelligence, pp. 1022–1027. Morgan Kaufmann, Chambery, France (1993)

    Google Scholar 

17. Van, D.G.E., Leccia, M., Dekker, S., Jalbert, N., Amodeo, D., Byers, H.: Role of Zyxin in Differential Cell Spreading and Proliferation of Melanoma Cells and Melanocytes. J. Invest. Dermatol. 118, 246–254 (2002)

    Article  Google Scholar 

18. Yagi, T., Morimoto, A., Eguchi, M., Hibi, S., Sako, M., Ishii, E., Mizutani, S., Imashuku, S., Ohki, M., Ichikawa, H.: Identification of a Gene Expression Signature Associated with Pediatric AML Prognosis. Blood 102, 1849–1856 (2003)

    Article  Google Scholar 

19. Banerjee, M., Mitra, S., Banka, H.: Evolutinary-Rough Feature Selection in Gene Expression Data. IEEE Transaction on Systems, Man, and Cybernetics, Part C: Application and Reviews 37, 622–632 (2007)

    Article  Google Scholar 

20. Momin, B.F., Mitra, S., Datta Gupta, R.: Reduct Generation and Classification of Gene Expression Data. In: Proceeding of First International Conference on Hybrid Information Technology (ICHICT 2006), pp. 699–708. IEEE Press, New York (2006)

    Chapter  Google Scholar 

21. Deb, K., Reddy, A.R.: Reliable Classification of Two Class Cancer Data Using Evolutionary Algorithms. BioSystems 72, 111–129 (2003)

    Article  Google Scholar 

22. Cho, S.B., Ryu, J.: Classification Gene Expression Data of Cancer Using Classifier Ensemble with Mutually Exclusive Features. In: Proceedings of the IEEE, Special Issue on Bioinformatics Part-I: Advances and Challenges, pp. 1744–1753. IEEE Press, New York (2002)

    Google Scholar 

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