A mixed factors model for dimension reduction and extraction of a group structure in gene expression data

Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004

Volumn , Issue , 2004, Pages 161-172

  Yoshida, Ryo ^a   Higuchi, Tomoyuki ^b   Imoto, Seiya ^c  

^a GRADUATE UNIVERSITY FOR ADVANCED STUDIES   (Japan)

^b INSTITUTE OF STATISTICAL MATHEMATICS   (Japan)

^c UNIVERSITY OF TOKYO   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

BOUNDARY VALUE PROBLEMS; COMPUTATIONAL METHODS; DATA COMPRESSION; EIGENVALUES AND EIGENFUNCTIONS; ENTROPY; ESTIMATION; GENES; MATHEMATICAL MODELS; PRINCIPAL COMPONENT ANALYSIS; PROBABILITY; TISSUE; VECTORS;

DATASETS; FACTOR ANALYSIS; GENE EXPRESSIONS; LEUKEMIA;

GENETIC ENGINEERING;

EID: 14044275730     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. H. Akaike, A new look at the statistical model identification, IEEE Transactions on Automatic Control, 19(30A), pp. 9-14, 1974.
2. A.A. Alizadeh, M.B. Eisen, R.E. Davis, C.Ma, I.S. Lossos, A. Rosenwald, J.C. Boldrick, H. Sabet, T. Tran, X. Yu, J.I. Powell, L. Yang, G.E. Marti, T. Moore, J. Hudson, L. Lu, R. Lewis, D.B Tibshirani, G. Sherlock, W.C. Chan, T.C. Greiner, D.D. Weisenberger, J.O. Armitage, R. Warnke, and L.M. Staudt, Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling, Nature, 403, pp. 503-511, 2000.
3. T.W. Anderson, An Introduction to multivariate statistical analysis, Wiley, New York, 1984.
4. Y. Barash, and N. Friedman, Context-specific Bayesian clustering for gene expression data, Proceedings of the Fifth Annual International Conference on Computational Biology, pp. 22-25, 2001.
5. M. Bittener, P. Meltzer, Y. Chen, Y. Jiang, E. Seftor, M. Hendrix, M. Radmacher, R. Simon, Z. Yakhini, A. Ben-Dor, N. Sampas, E. Dougherty, E. Wang, F. Marincola, C. Gooden, J. Lueders, A. Glatfelter, P. Pollock, J. Carpten, E. Gillanders, D. Leja, K. Dietrich, C. Beaudry, M. Berens, D. Alberts, and V. Sondak, Molecular classification of cutaneous malignant melanoma by gene expression profiling, Nature, 406, pp. 536-540, 2000.
6. W.C. Chang, On using principal components before separating a mixture of two multivariate normal distributions, Applied Statistics, 32, pp. 267-275, 1983.
7. M.L. Chow, E.J. Moler, and I.S. Mian, Identifying marker genes in transcription profiling data using a mixture of feature relevance experts, Physiol. Genomics, 5, pp. 99-111, 2001.
8. A.P. Dempster, N.M. Laird, and D.B. Rubin, Maximum likelihood from incomplete data via the EM algorithm (with discussion), Journal of the Royal Statistical Society B, 39, pp. 1-38, 1977.
9. S. Dudoit, J. Fridlyand, and T.P. Speed, Comparison of discrimination methods for the classification of tumors using gene expression data, Journal of the American Statistical Association, 97(457), pp. 77-87, 2002.
10. C. Fraley and A.E. Raftery, Model-based clustering, discriminant analysis, and density estimation, Journal of the American Statistical Association, 97(458), pp. 611-631, 2002.
11. D. Ghosh and A.M. Chinnaiyan, Mixture modeling of gene expression data from microarray experiments, Bioinformatics, 18(2), pp. 275-286, 2002.
12. T.R. Goloub, D.K. Slonim, P. Tunayo, C. Huard, M. Gassenbeck, J.P. Mersirov, H. Coller, M.L. Loh, J.R. Downing, M.A. Caligiuri, C.D. Bloomfield, and E.S. Lander, Molecular classification of cancer: class discovery and class prediction by gene expression monitoring, Science, 286, pp. 531-537, 1999.
13. I. Holmes and W.J. Bruno, Finding regulatory elements using joint likelihoods for sequence and expression profile data, Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, pp. 19-23, 2000.
14. E.L. Lehmann, Theory of point estimation, Wiley, New York, 1983.
15. E. L. Lehmann, Testing statistical hypotheses, Wiley, New York, 1986.
16. G.J. McLachlan, R.W. Bean, and D. Peel, A mixture model-based approach to the clustering of microarray expression data, Bioinformatics, 18(3), pp. 413-422, 2002.
17. G.J. McLachlan and D. Peel, Finite Mixture Models, Wiley, New York, 1997.
18. B.D. Ripley, Pattern recognition and neural networks, Cambridge University Press, Cambridge, 1996.
19. D.T. Ross, U. Scherf, M.B. Eisen, C.M. Perou, P. Spellman, V. Iyer, S.S. Jeffrey, M.V. deRijin, M. Waltham, A. Pergamenshikov, C.F. Lee, D. Lashkari, D. Shalon, T.G. Myers, J.N. Weinstein, D. Botstein, and P.O. Brown, Systematic variation in gene expression patterns in human cancer cell lines, Nature Genetics, 24, pp. 227-234, 2000.
20. G. Schwartz, Estimating the dimension of a model, Annals of Statistics, 6, pp. 461-464, 1978.
21. M.E. Tipping and C.M. Bishop, Mixtures of probabilistic principal component analyzers, Neural Computation, 11, pp. 443-482, 1999.