Multiobjective fuzzy biclustering in microarray data: Method and a new performance measure

2008 IEEE Congress on Evolutionary Computation, CEC 2008

Volumn , Issue , 2008, Pages 1536-1543

  Maulik, Ujjwal ^a   Mukhopadhyay, Anirban ^b   Bandyopadhyay, Sanghamitra ^c   Zhang, Michael Q ^d,e   Zhang, Xuegong ^e  

^a JADAVPUR UNIVERSITY   (India)

^b UNIVERSITY OF KALYANI   (India)

^c INDIAN STATISTICAL INSTITUTE   (India)

^d Howard Hughes Medical Institute Cold Spring Harbor Laboratory Cold Spring Harbor   (United States)

^e TSINGHUA UNIVERSITY   (China)

Author keywords

Expression profile variance; Fuzzy biclustering; Fuzzy K medoids; Mean squared residue; Multiobjective genetic algorithm; Statistical difference from background; Variable string length

Indexed keywords

BIOASSAY; BOOLEAN FUNCTIONS; FUZZY CLUSTERING; FUZZY SETS; GENES; GENETIC ALGORITHMS; MICROARRAYS; MULTIOBJECTIVE OPTIMIZATION; PROBABILISTIC LOGICS;

EXPRESSION PROFILE VARIANCE; FUZZY BICLUSTERING; FUZZY K-MEDOIDS; MEAN SQUARED RESIDUE; MULTIOBJECTIVE GENETIC ALGORITHM; STATISTICAL DIFFERENCE FROM BACKGROUND; VARIABLE STRING LENGTH;

ALGORITHMS;

EID: 55749112826     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2008.4630996     Document Type: Conference Paper

References (26)

1. A. K. Jain and R. C. Dubes, Algorithms for Clustering Data. Englewood Cliffs, NJ: Prentice-Hall, 1988,
2. J. T. Tou and R. C. Gonzalez, Pattern Recognition Principles. Reading: Addison-Wesley, 1974.
3. P. Tamayo, D. Slonim, J. Mesirov, Q. Zhu, S. Kitareewan, E. Dmitrovsky, E. Lander, and T. Golub, "Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation," in Proc. Nat. Academy of Sciences, vol. 96, USA, 1999, pp. 2907-2912,
4. U. Maulik and S. Bandyopadhyay, "Genetic algorithm based clustering technique," Pattern Recognition, vol. 33, pp. 1455-1465, 2000.
5. A. Mukhopadhyay, U. Maulik, and S. Bandyopadhyay, Multiohjective Evolutionary Approach to Fuzzy Clustering of Microarray Data. World Scientific, 2007, ch. 13, pp. 303-326,
6. S. Bandyopadhyay, A. Mukhopadhyay, and U. Maulik, "An improved algorithm for clustering gene expression data," Bioinformatics, vol. 23, no. 21, pp. 2859-2865, 2007,
7. Y. Cheng and G. M. Church, "Biclustering of gene expression data," in Proc. Int. Conf. on Intelligent Systems for Molecular Biology {ISMB'OO), 2000, pp. 93-103.
8. A. Tanay, R. Sharan, and R. Shamir, Biclustering algorithms: a survey, 2006.
9. R. Krishnapuram, A. Joshi, and L. Yi, "A fuzzy relative of the kmedoids algorithm with application to document and snippet clustering," in Proc. IEEE Intl. Conf. Fuzzy Systems - FUZZ-IEEE 99, Seoul, South Korea, 1999, pp. 1281-1286.
10. K. Deb, Multi-objective Optimization Using Evolutionary Algorithms. England: John Wiley and Sons, Ltd, 2001.
11. C. A. Coello Coello, "A comprehensive survey of evolutionary-based multiobjective optimization techniques," Knowledge and Information Systems, vol. 1, no, 3, pp. 129-156, 1999.
12. S. Bandyopadhyay, U. Maulik, and A. Mukhopadhyay, "Multiobjective genetic clustering for pixel classilication in remote sensing imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 5, pp. 1506-1511, 2007.
13. S. Bandyopadhyay, S. Saha, U. Maulik, and K. Deb, "A simulated annealing based multi-objective optim ization algorithm: AMOS A," IEEE Trans. Evolutionary Computation, (in press).
14. K. Deb, A. Pratap, S. Agrawal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," IEEE Transactions on Evolutionary Computation, vol. 6, pp. 182-197, 2002,
15. J. Hartigan, "Direct clustering of a data matrix," J. American Statistical Association, vol. 67, no. 337, pp. 123-129, 1972.
16. J. Yang, W. Wang, H. Wang, and P. Yu, "Enhanced biclustering on expression data," in Proc. 3rd IEEE conf. Bioinformatics and Bioengineering (BIBEV3), 2003, pp. 321-327,
17. F. Angiulli, E. Cesario, and C. Pizzuti, "Gene expression biclustering using random walk strategies," in Proc. 7th Int. Conf. on Data Warehousing and Knowledge Discovery (DAWAK'05), Copenhagen, Denmark, 2005.
18. G. Getz, E. Levine, and E. Domany, "Coupled two-way cluster analysis of gene microarray data," in Proc. National Academy of Sciences, USA, 2000, pp. 12079-12 084.
19. A. Tanay, R. Sharan, and R. Shamir, "Discovering statistically significant biclusters in gene expression data," Bioinformatics, vol. 18, pp. S136-S144, 2002.
20. C. A. Coello Coello, A. Carlos, and A. D. Christiansen, "An approach to multiobjective optimization using genetic algorithms," in Intelligent Engineering Systems Through Artificial Neural Networks, vol. 5, ASME Press, St. Louis Missouri, USA, 1995, pp. 411-116,
21. E. Zitzler, M. Laumanns, and L. Thiele, "SPEA2: Improving the Strength Pareto Evolutionary Algorithm," Gloriastrasse 35, CH-8092 Zurich, Switzerland, Tech. Rep. 103, 2001.
22. E. Zitzler and L. Thiele, "An evolutionary algorithm for multiobjective optimization: The strength pareto approach," Gloriastrasse 35, CH-8092 Zurich. Switzerland, Tech. Rep. 43, 1998.
23. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum, 1981,
24. H. Cho, I. Dhilon, Y. Guan, and S. Sra, "Minimum sum-squared residue co-clustering of gene expression data," in Proc. 4th SIAM Int. Conf. on Data Mining, 2004.
25. W. Shannon, R. Culverhouse, and J. Duncan, "Analyzing microarray data using cluster analysis," Pharmacogenomics, vol. 4, no. 1, pp. 41-51, 2003.
26. S. Y. Kim, J. W. Lee, and J. S. Bae, "Effect of data normalization on fuzzy clustering of dna microarray data," BMC Bioinformatics, vol. 7, no. 134, 2006.