1Schena M, Shalon D, Davis R W, et al. Quantitative monitoring of gene expressionpatterns with a complementary DNA microarray[J].Science, 1999, 270(5235):467-470.
2Tavazoie S, Hughes D, Campbell M J, et al. Systematic determination of genetic networkarchitecture[J]. Nature Genetics,1999, 22 (3): 281-285.
3Sherlock G. Analysis of large-scale geneexpression data[J]. Curr Opin Immunol, 2000, 12 (2): 201-205.
4Luo F, Khan L, Bastan F, et al. A dynamically growing self-organizing tree (DGSOT) forhierarchical clustering gene expression profiles[J].Bioinformatics, 2004, 20(16):2605-2617.
5Futschik M E, Kasabov N K. Fuzzy clusteringof gene expression data. In: Proceedingsof 2002 IEEE International Conference on FuzzySystems[J]. vol. 1. Piscataway, NJ, USA: IEEE, 2002:414-419.
6Gasch A P, Eisen N B. Exploring the con dition co-regulation of yeast gene expressionthrough fuzzy K-means clustering[J]. Genome Biology, 2002, 3 (11):1-22.
7Mukhopadhyay A, Maulik U, Bandyopadhyay S. Efficient two-stage fuzzy clustering of microarraygene expression data. In: The 9th InternationalConference on Information Technology[J].Los Alamitos, CA, USA: IEEE Computer Society, 2006:11-14.
8Cho S B, Yoo S H. Fuzzy Bayesian validation for cluster analysis of yeast cell-cycledata[J]. Pattern Recognition, 2006,39 (12):2405-2414.
9Maraziotis I A, Dragomir A, Bezerianos A. Semi Supervised Fuzzy Clustering Networks forConstrained Analysis of Time-Series 6ene ExpressionData[J]. artificial neural networks ICANN2006, 2006, 4132: 818-826.
10Yang W, Rueda L, Ngom A. A Simulated Annealing Approach to Find the Optimal Parametersfor Fuzzy Clustering Microarray Data. In: Proceedings of the ⅩⅩⅤ International Conferenceon the Chilean Computer Science Society[M].Washington. De. USA: IEEE Computer Society. 2005: 45-54.