Modelling transcriptional regulation with a mixture of factor analyzers and variational bayesian expectation maximization

Eurasip Journal on Bioinformatics and Systems Biology

Volumn 2009, Issue , 2009, Pages

  Lin, Kuang ^a   Husmeier, Dirk ^a  

^a BIOMATHEMATICS AND STATISTICS SCOTLAND   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 68149168023     PISSN: 16874145     EISSN: 16874153     Source Type: Journal    
DOI: 10.1155/2009/601068     Document Type: Article

References (55)

1. Bussemaker H. J., Li H., Siggia E. D., Regulatory element detection using correlation with expression Nature Genetics 2001 27 2 167 171
2. Conlon E. M., Liu X. S., Lieb J. D., Liu J. S., Integrating regulatory motif discovery and genome-wide expression analysis Proceedings of the National Academy of Sciences of the United States of America 2003 100 6 3339 3344
3. Beer M. A., Tavazoie S., Predicting gene expression from sequence Cell 2004 117 2 185 198
4. Phuong T. M., Lee D., Lee K. H., Regression trees for regulatory element identification Bioinformatics 2004 20 5 750 757
5. Segal E., Yelensky R., Koller D., Genome-wide discovery of transcriptional modules from DNA sequence and gene expression Bioinformatics 2003 19 supplement 1 i273 i282
6. Middendorf M., Kundaje A., Wiggins C., Freund Y., Leslie C., Predicting genetic regulatory response using classification Bioinformatics 2004 20 supplement 1 i232 i240
7. Middendorf M., Kundaje A., Shah M., Freund Y., Wiggins C. H., Leslie C., Motif discovery through predictive modeling of gene regulation Proceedings of the 9th Annual International Conference on Research in Computational Molecular Biology (RECOMB 05) May 2005 Cambridge, Mass, USA 538 552
8. Ruan J., Zhang W., A bi-dimensional regression tree approach to the modeling of gene expression regulation Bioinformatics 2006 22 3 332 340
9. Raychaudhuri S., Stuart J. M., Altman R. B., Principal components analysis to summarize microarray experiments: application to sporulation time series Pacific Symposium on Biocomputing 2000 5 455 466
10. Liebermeister W., Linear modes of gene expression determined by independent coponent analysis Bioinformatics 2002 18 1 51 60
11. Liao J. C., Boscolo R., Yang Y.-L., Tran L. M., Sabatti C., Roychowdhury V. P., Network component analysis: reconstruction of regulatory signals in biological systems Proceedings of the National Academy of Sciences of the United States of America 2003 100 26 15522 15527
12. Kao K. C., Yang Y.-L., Boscolo R., Sabatti C., Roychowdhury V., Liao J. C., Transcriptome-based determination of multiple transcription regulator activities in Escherichia coli by using network component analysis Proceedings of the National Academy of Sciences of the United States of America 2004 101 2 641 646
13. Harbison C. T., Gordon D. B., Lee T. I., Transcriptional regulatory code of a eukaryotic genome Nature 2004 430 7004 99 104
14. Bailey T. L., Elkan C., Fitting a mixture model by expectation maximization to discover motifs in biopolymers Proceedings of the 2nd International Conference on Intelligent Systems for Molecular Biology (ISMB 94) August 1994 Stanford, Calif, USA 28-36
15. Hughes J. D., Estep P. W., Tavazoie S., Church G. M., Computational identification of cis -regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae Journal of Molecular Biology 2000 296 5 1205 1214
16. Sabatti C., James G. M., Bayesian sparse hidden components analysis for transcription regulation networks Bioinformatics 2006 22 6 739 746
17. Sanguinetti G., Lawrence N. D., Rattray M., Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities Bioinformatics 2006 22 22 2775 2781
18. Pournara I., Wernisch L., Factor analysis for gene regulatory networks and transcription factor activity profiles BMC Bioinformatics 2007 8, article 61 1 20
19. Shi Y., Simon I., Mitchell T., Bar-Joseph Z., Vingron M., Wong L., A combined expression-interaction model for inferring the temporal activity of transcription factors 4955 Proceedings of the 12th Annual International Conference on Research in Computational Molecular Biology (RECOMB 08) March-April 2008 Singapore Springer 82 97 Lecture Notes in Computer Science
20. Remnyi A., Schler H. R., Wilmanns M., Combinatorial control of gene expression Nature Structural Molecular Biology 2004 11 9 812 815
21. Yu X., Lin J., Zack D. J., Qian J., Identification of tissue-specific cis -regulatory modules based on interactions between transcription factors BMC Bioinformatics 2007 8, article 437 1 13
22. Boulesteix A.-L., Strimmer K., Predicting transcription factor activities from combined analysis of microarray and ChIP data: a partial least squares approach Theoretical Biology Medical Modelling 2005 2, article 23 1 12
23. Beal M. J., Variational algorithms for approximate Bayesian inference, Ph.D. thesis 2003 London, UK Gatsby Computational Neuroscience Unit, University College London
24. Ghahramani Z., Hinton G. E., The EM algorithm for mixtures of factor analyzers 1996 CRG-TR-96-1 Toronto, Canada Department of Computer Science, University of Toronto
25. Nielsen F. B., Variational approach to factor analysis and related models, M.S. thesis 2004 Lyngby, Denmark Informatics and Mathematical Modelling, Technical University of Denmark. http://www2.imm.dtu.dk/pubdb/views/ publicationdetails.php?id=3182
26. Ghahramani Z., Beal M. J., Solla S. A., Leen T. K., Müller K.-R., Variational inference for Bayesian mixtures of factor analysers Advances in Neural Information Processing Systems 1999 Cambridge, Mass, USA The MIT Press 449 455
27. West M., Bayesian factor regression models in the large p, small n paradigm Bayesian Statistics 2003 7 Oxford, UK Oxford University Press 733 742
28. McLachlan G. J., Bean R. W., Ben-Tovim Jones L., Extension of the mixture of factor analyzers model to incorporate the multivariate t -distribution Computational Statistics Data Analysis 2007 51 11 5327 5338
29. Huber W., von Heydebreck A., Sltmann H., Poustka A., Vingron M., Variance stabilization applied to microarray data calibration and to the quantification of differential expression Bioinformatics 2002 18 supplement 1 S96 S104
30. Fokoué E., Titterington D. M., Mixtures of factor analysers. Bayesian estimation and inference by stochastic simulation Machine Learning 2003 50 1-2 73 94
31. Green P. J., Reversible jump Markov chain Monte Carlo computation and Bayesian model determination Biometrika 1995 82 4 711 732
32. Ueda N., Nakano R., Ghahramani Z., Hinton G. E., SMEM algorithm for mixture models Neural Computation 2000 12 9 2109 2128
33. Guelzim N., Bottani S., Bourgine P., Kps F., Topological and causal structure of the yeast transcriptional regulatory network Nature Genetics 2002 31 1 60 63
34. Lee T. I., Rinaldi N. J., Robert F., Transcriptional regulatory networks in Saccharomyces cerevisiae Science 2002 298 5594 799 804
35. Spellman P. T., Sherlock G., Zhang M. Q., Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization Molecular Biology of the Cell 1998 9 12 3273 3297
36. Gasch A. P., Spellman P. T., Kao C. M., Genomic expression programs in the response of yeast cells to environmental changes Molecular Biology of the Cell 2000 11 12 4241 4257
37. Mnaimneh S., Davierwala A. P., Haynes J., Exploration of essential gene functions via titratable promoter alleles Cell 2004 118 1 31 44
38. Teixeira M. C., Monteiro P., Jain P., The YEASTRACT database: a tool for the analysis of transcription regulatory associations in Saccharomyces cerevisiae Nucleic Acids Research 2006 34, database issue D446 D451
39. Kaiser H. F., The varimax criterion for analytic rotation in factor analysis Psychometrika 1958 23 3 187 200
40. Chang C., Ding Z., Hung Y. S., Fung P. C. W., Fast network component analysis (FastNCA) for gene regulatory network reconstruction from microarray data Bioinformatics 2008 24 11 1349 1358
41. Cowles M. K., Carlin B. P., Markov chain Monte Carlo convergence diagnostics: a comparative review Journal of the American Statistical Association 1996 91 434 883 904
42. Bishop C. M., Pattern Recognition and Machine Learning 2006 Singapore Springer
43. Friedman J. H., Meulman J. J., Clustering objects on subsets of attributes (with discussion) Journal of the Royal Statistical Society: Series B 2004 66 4 815 849
44. Lazzeroni L., Owen A., Plaid models for gene expression data Statistica Sinica 2002 12 1 61 86
45. Eisen M. B., Spellman P. T., Brown P. O., Botstein D., Cluster analysis and display of genome-wide expression patterns Proceedings of the National Academy of Sciences of the United States of America 1998 95 25 14863 14868
46. Hastie T., Tibshirani R., Friedman J., The Elements of Statistical Learning 2001 Berlin, Germany Springer
47. Grossmann S., Bauer S., Robinson P. N., Vingron M., Apostolico A., Guerra C., Istrail S., Pevzner P. A., Waterman M. S., An improved statistic for detecting over-represented gene ontology annotations in gene sets 3909 Proceedings of the 10th Annual International Conference on Research in Computational Molecular Biology (RECOMB 06) April 2006 Venice, Italy Springer 85 98 Lecture Notes in Computer Science
48. Papoulis A., Probability, Random Variables, and Stochastic Processes 1991 3rd Singapore McGraw-Hill
49. Grzegorczyk M., Husmeier D., Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move Machine Learning 2008 71 2-3 265 305
50. Jasra A., Holmes C. C., Stephens D. A., Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling Statistical Science 2005 20 1 50 67
51. Kim T. S., Kim H. Y., Yoon J. H., Kang H. S., Recruitment of the Swi/Snf complex by Ste12-Tec1 promotes Flo8-Mss11-mediated activation of STA1 expression Molecular and Cellular Biology 2004 24 21 9542 9556
52. Barabsi A.-L., Oltvai Z. N., Network biology: understanding the cells functional organization Nature Reviews Genetics 2004 5 2 101 113
53. Teh Y. W., Jordan M. I., Beal M. J., Blei D. M., Hierarchical dirichlet processes 2004 653 Berkeley, Calif, USA Department of Statistics, University of California
54. Xing E. P., Jordan M. I., Sharan R., Bayesian haplotype inference via the dirichlet process Journal of Computational Biology 2007 14 3 267 284
55. Kuhn H. W., The Hungarian method for the assignment problem Naval Research Logistics 1955 2 1-2 83 97