Estimation Distribution of Algorithm for Fuzzy Clustering Gene Expression Data

With the rapid development of genome projects, clustering of gene expression data is a crucial step in analyzing gene function and relationship of conditions. In this paper, we put forward an estimation of distribution algorithm for fuzzy clustering gene expression data, which combines estimation of distribution algorithms and fuzzy logic. Comparing with sGA, our method can avoid many parameters and can converge quickly. Tests on real data show that EDA converges ten times as fast as sGA does in clustering gene expression data. For clustering accuracy, EDA can get a more reasonable result than sGA does in the worst situations although both methods can get the best results in the best situations.

[1]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[2]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[3]  Mitsuo Gen,et al.  Genetic algorithm for fuzzy clustering , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[4]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[5]  Enrique H. Ruspini,et al.  A New Approach to Clustering , 1969, Inf. Control..

[6]  T. M. Murali,et al.  Extracting Conserved Gene Expression Motifs from Gene Expression Data , 2002, Pacific Symposium on Biocomputing.

[7]  Chiang-Ching Huang,et al.  Clear Cell Sarcoma of the Kidney: Up-regulation of Neural Markers with Activation of the Sonic Hedgehog and Akt Pathways , 2005, Clinical Cancer Research.

[8]  Vladimir Pavlovic,et al.  RankGene: identification of diagnostic genes based on expression data , 2003, Bioinform..

[9]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[10]  Andrew P. Sage,et al.  Uncertainty in Artificial Intelligence , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[11]  Pierre Baldi,et al.  DNA Microarrays and Gene Expression - From Experiments to Data Analysis and Modeling , 2002 .

[12]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[13]  Max Henrion,et al.  Propagating uncertainty in bayesian networks by probabilistic logic sampling , 1986, UAI.

[14]  J. C. Dunn,et al.  A Graph Theoretic Analysis of Pattern Classification via Tamura's Fuzzy Relation , 1974, IEEE Trans. Syst. Man Cybern..