Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Biological or biochemical
Reexamination Certificate
2005-11-15
2005-11-15
Brusca, John S. (Department: 1631)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Biological or biochemical
C702S020000
Reexamination Certificate
active
06965831
ABSTRACT:
A novel coupled two-way clustering approach to gene microarray data analysis, for identifying subsets of the genes and samples, such that when one of these items is used to cluster the other, stable and significant partitions emerge. The method of the present invention preferably uses iterative clustering in order to execute this search in an efficient way. This approach is especially suitable for gene microarray data, where the contributions of a variety of biological mechanisms to the gene expression levels are entangled in a large body of experimental data. The method of the present invention was applied to two gene microarray data sets, on colon cancer and leukemia. By identifying relevant subsets of the data and focusing on these subsets, partitions and correlations were found that were masked and hidden when the full data set was used in the analysis.
REFERENCES:
patent: 6021383 (2000-02-01), Domany et al.
te Poele et al. RNA synthesis block by 5,6-dichloro-1-beta-D-ribofuranosylbenzimidazole (DRB) triggers p53-dependent apoptosis in human colon carcinoma cells. Oncogene vol. 18, pp. 5765-5772 (1999).
J. L. DeRisi, VR Iyer and PO Brown. Exploring the metabolic and genetic control of gene expression on a genomic scale.Science, 278:680-686, 1997.
U. Alon et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays.PNAS, 96:6745-6750, 1999.
M.B. Eisen et al. Cluster analysis and display of genome-wide expression patterns.PNAS, 95:14683-14686, 1998.
T.R. Golub et al. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring.Science, 286:531-537, 1999.
C.M. Perou et al. Distinctive gene expression patterns in human mammary epithelial cells and breast cancers.PNAS, 96:9212-9217, 1999.
E.S. Lander. Array of hope.Nature Genetics, 21:3-4, 1999.
M.Q. Zhang. Promoter analysis of co-regulated genes in the yeast genome.Comput. Chem., 23:233-250, 1999.
M. Blatt et al. Super-paramagnetic clustering of data.Physical Review Letters, 76:3251-3254, 1996.
E. Domany. Super-paramagnetic clustering of data-the definitive solution of an ill-posed problem.Physica A, 263:158-169, 1999.
G. Getz et al. Super-paramagnetic clustering of yeast gene expression profiles.Physica A, 279 (2000) 457-464.
G. Getz et al. Coupled two way clustering of gene microarray dataPNAS, Oct. 24, 2000, vol. 97, No. 22, pp 12079-12084.
M. Blatt et al. Data clustering using a model granular magnet.Neural Computation, 9:1805-1842, 1997.
S. Wang and RH Swendsen. Cluster Monte-Carlo Algorithms.Physica A., 167:565-579, 1990.
M. Schena et al. Parallel human genome analysis: microarray-based expression monitoring of 1000 genes.PNAS, 93:10614-10619, 1996.
Califano, et al., Analysis of Gene Expression Microarrays for Phenotype Classification, Proc. Int. Conf. Intell. Syst. Mol. Biol. (2000) vol. 8, pp. 75-85.
Y. Cheng et al., Biclustering of Expression Data, Proc. Int. Conf. Intell. Syst. Mol. Biol. (2000) vol. 8, pp. 93-103.
Domany Eytan
Getz Gad
Levine Erel
Brusca John S.
G.E. Ehrlich (1995) Ltd.
Yeda Research and Development Co. Ltd.
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