Biclustering microarray data by Gibbs sampling

Bioinformatics

Volumn 19, Issue SUPPL. 2, 2003, Pages

  Sheng, Qizheng ^a   Moreau, Yves ^a   De Moor, Bart ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; AMINO ACID SEQUENCE; CONFERENCE PAPER; DNA MICROARRAY; DNA SEQUENCE; FINGER DERMATOGLYPHICS; MATHEMATICAL ANALYSIS; PRIORITY JOURNAL; PROBABILITY; STATISTICAL MODEL;

EID: 4444367133     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/btg1078     Document Type: Conference Paper

References (12)

1. Armstrong, S.A., Staunton, J.E., Silverman, L.B., Pieters, R., den Boer, M.L., Minden, M.D., Sallan, S.E., Lander, E.S., Golub, T.R. and Korsmeyer, S.J. (2002) MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia. Nat. Genet., 30, 41-47.
2. Ben-Dor, A., Friedman, N. and Yakhini, Z. (2001) Class discovery in gene expression data. Proc. 5th Annual Intl. Conf. on Computational Biology, 5, 31-38.
3. Busygin, S., Jacobsen, G. and Krämer, E. (2002) Double conjugated clustering applied to leukemia microarray data. 2nd SIAM ICDM, Workshop on Clustering High Dimensional Data.
4. Casella, G. and George, E.I. (1992) Explaining the Gibbs sampler. J. Amer. Stat., 46, 167-174.
5. Cheng, Y. and Church, G.M. (2000) Biclustering of expression data. ISMB 2000 proceedings, 93-103.
6. Getz, G., Levine, E. and Domany, E. (2000) Coupled two-way clustering analysis of gene microarray data. Proc. Natl Acad. Sci. USA, 97, 12079-12084.
7. Lawrence, C.E., Altschul, S.F., Boguski, M.S., Liu, J.S., Neuwald, A.F. and Wooton, J.C. (1993) Detecting subtle sequence signals: a Gibbs sampling strategy for multiple alignments. Science, 262, 208-214.
8. Lazzeroni, L. and Owen, A. (1999) Plaid models for gene expression data. Technical report, Stanford University, Statistics.
9. Liu, J.S., Neuwald, A.F. and Lawrence, C.E. (1995) Bayesian models for multiple local sequence alignment and Gibbs sampling strategies. J. Amer. Stat., 90, 1156-1170.
10. Segal, E., Taskar, B., Gasch, A., Friedman, N. and Koller, D. (2001) Rich probabilistic models for gene expression. Bioinformatics, 17, 243S-249S.
11. Tanay, A., Sharan, R. and Shamir, R. (2002) Discovering statistically significant biclusters in gene expression data. Bioinformatics, 18, 136S-144S.
12. Thijs, G., Marchal, K., Lescot, M., Rombauts, S., De Moor, B., Rouzé, P. and Moreau, Y. (2002) A Gibbs sampling method to detect overrepresented motifs in the upstream regions of coexpressed genes. J. Comput. Biol., 9, 447-464.