An unsupervised conditional random fields approach for clustering gene expression time series

Bioinformatics

Volumn 24, Issue 21, 2008, Pages 2467-2473

  Li, Chang Tsun ^a   Yuan, Yinyin ^a   Wilson, Roland ^a  

^a UNIVERSITY OF WARWICK   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

GALACTOSE;

ACCURACY; ARTICLE; CELL CYCLE G2 PHASE; CELL CYCLE M PHASE; CELL CYCLE S PHASE; COMPUTER PROGRAM; GENE CLUSTER; GENE EXPRESSION PROFILING; GENETIC ALGORITHM; GENETIC ANALYSIS; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; PRIORITY JOURNAL; PROBABILITY; SACCHAROMYCES CEREVISIAE; TIME SERIES ANALYSIS;

ALGORITHMS; CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; GENE EXPRESSION PROFILING; MODELS, STATISTICAL;

EID: 54949112147     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btn375     Document Type: Article

References (24)

1. Ashburner,M. et al. (2000) Gene ontology: Tool for the unification of biology. Nat. Genet., 25, 25-29.
2. Bar-Joseph,Z. et al. (2002) A new approach to analyzing gene expression time series data. In Proceedings of the Annual International Conference on Computational Molecular Biology, RECOMB, pp. 39-48.
3. Boutros,P.C. and Okey,A.B. (2005) Unsupervised pattern recognition: An introduction to the whys and wherefores of clustering microarray data. Brief. Bioinform., 6, 331-343.
4. Cho,R.J. et al. (1998) A genome-wide transcriptional analysis of the mitotic cell cycle. Mol. Cell, 2, 65-73.
5. Culotta,A. et al. (2005) Gene prediction with conditional random fields.
6. Dojer, N. et al. (2006) Applying dynamic Bayesian networks to perturbed gene expression data. BMC Bioinformatics, 7.
7. Ernst,J. et al. (2005) Clustering short time series gene expression data. Bioinformatics, 21(Suppl. 1).
8. Fraley,C. and Raftery,A. (1999) Mclust: Software for model-based cluster and discriminant analysis.
9. Fraley,C. and Raftery,A. (2002) Model-based clustering, discriminant analysis, and density estimation. J. Am. Stat. Assoc., 97, 611-631.
10. Geman,S. and Geman,D. (1984) Stochastic relaxation, Gibbs distribution, and Bayesian restoration of images. 6, 721-741.
11. Heard,N.A. et al. (2006) A quantitative study of gene regulation involved in the immune response of anopheline mosquitoes: An application of bayesian hierarchical clustering of curves. J. Am. Stat. Assoc. 101, 18-29.
12. Hubert,L. and Arabie,P. (1985) Comparing partitions. J. Classif., 2, 193-218.
13. Husmeier,D. (2003) Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics, 19, 2271-2282.
14. Ideker,T. et al. (2001) Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science, 292 929-934.
15. Ji,X. et al. (2003) Mining gene expression data using a novel approach based on hidden markov models. FEBS Lett., 542, 125-131.
16. Lafferty,J.D. et al. (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. In Proceedings of the Eighteenth International Conference on Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp. 282-289.
17. Luan,Y. and Li,H. (2003) Clustering of time-course gene expression data using a mixed-effects model with B-splines. Bioinformatics, 19 474-482.
18. Ma,P. et al. (2006) A data-driven clustering method for time course gene expression data. Nucleic Acids Res., 34, 1261-1269.
19. Medvedovic,M. et al. (2004) Bayesian mixture model based clustering of replicated microarray data. Bioinformatics, 20 1222-1232.
20. Ng,S.K. et al. (2006) A mixture model with random-effects components for clustering correlated gene-expression profiles. Bioinformatics, 22, 1745-1752.
21. Schliep,A. et al. (2005) Analyzing gene expression time-courses. IEEE/ACM Trans. Comput. Biol. Bioinform., 2, 179-193.
22. Slonim,D.K. (2002) From patterns to pathways: Gene expression data analysis comes of age. Nat. Genet., 32 (Suppl), 502-508.
23. Tjaden,B. (2006) An approach for clustering gene expression data with error information. BMC Bioinformatics, 7, 17.
24. Wu,F.X. et al. (2005) Dynamic model-based clustering for time-course gene expression data. J. Bioinform. Comput. Biol., 3, 821-836.