Two-phase clustering strategy for gene expression data sets

Proceedings of the ACM Symposium on Applied Computing

Volumn 1, Issue , 2006, Pages 145-150

  Habich, Dirk ^a   Wächter, Thomas ^a   Lehner, Wolfgang ^a   Pilarsky, Christian ^a  

^a DRESDEN UNIVERSITY OF TECHNOLOGY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DATA REDUCTION; GENES; STATISTICAL METHODS;

BIOLOGICAL QUALITY MEASURES; MICROARRAY DATA SETS; TWO-PHASE CLUSTERING STRATEGY;

GENETIC ENGINEERING;

EID: 33751026485     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1141277.1141311     Document Type: Conference Paper

References (17)

1. J. Bryan. Problems in gene clustering based on gene expression data. Journal of Multivariate Analysis, 90:44-66, 2004.
2. Y. Cheng and G. M. Church. Biclustering of expression data. In Proc. of ISMB, pages 93-103, 2000.
3. H. Chipman, T. J. Hastie, and R. Tibshirani. Clustering microarray data. In Terry Speed, editor, Statistical Analysis of Gene Expression Microarray Datas, pages 159-200. Chapman and Hall/CRC, 2003.
4. D. J. Cook, G. H. Guyatt, A. Laupacis, D. L. Sackett, and R. J. Goldberg. Clinical recommendations using levels of evidence for antithrombotic agents. Chest, 108(4 Suppl):227-230, 1995.
5. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proc. of KDD, pages 226-231, 1996.
6. H. Friess, J. Ding, J. Kleeff, L. Fenkell, J. A. Rosinski, A. Guweidhi, J. F. Reidhaar-Olson, M. Korc, J. Hammer, and M. W. Büchler. Microarray-based identification of differentially expressed groth- and metastasis-associated genes in pancreatic cancer. CMLS Cellular and Molecular Life Science, 60:1180-1199, 2003.
7. R. Hoffmann, T. Seidl, and M. Dugas. Profound effect of normalization on detection of differentially expressed genes in oligonucleotide microarray data analysis. Genome Biology, 3:0033.1-0033.11, 2002.
8. E. Januzaj, H.-P. Kriegel, and M. Pfeifle. Dbdc: Density based distributed clustering. In Proc. of EDBT, pages 88-105, 2004.
9. E. L. Johnson and H. Kargupta. Collective, hierarchical clustering from distributed, heterogeneous data. In Proc. of the Large-Scale Parallel Data Mining, pages 221-244, 1999.
10. H. Kargupta and P. Chan. Advances in Distributed and Parallel Knowledge Discovery. AAAI/MTT Press, 2000.
11. L. Kaufmann and P. Rousseeuw. Finding Groups in Data: An Introduction to Cluster Analysis. Wiley New York, 1990.
12. C. D. Logsdon, D. M. Simeone, C. Binkley, T. Arumugam, J-K. Greenson, T. J. Giordano, D. E. Misek, and S. Hanash. Molecular profiling of pancreatic adenocarcinoma and chronic pancreatitis identifies multiple genes differentially regulated in pancreatic cancer. Cancer Research, 63:2649-2657, 2003.
13. D.W. Scott. Multivariate Density Estimation. Wiley and Sons, 1992.
14. B. W. Silverman. Density Estimation for Statistics and Data Analysis. Chapman & Hall, 1986.
15. Y. Zeng, J. Tang, J. Garcia-Frias, and G. R. Gao. An adaptive meta-clustering approach: Combining the information from different clustering results. In Proc. of. CSB, pages 276-287, 2002.
16. T. Zhang, R. Ramakrishnan, and M. Livny. Birch: An efficient data clustering method for very large databases. In Proc. of SIGMOD, pages 103-114, 1996.
17. T. Zhang, R. Ramakrishnan, and M. Livny. Fast density estimation using cf-kernel for very large databases. In Proc. of KDD, pages 312-316, 1999.