Bayesian sparse hidden components analysis for transcription regulation networks

Bioinformatics

Volumn 22, Issue 6, 2006, Pages 739-746

  Sabatti, Chiara ^a   James, Gareth M ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BACTERIAL DNA; REGULATOR PROTEIN; TRANSCRIPTION FACTOR;

ARTICLE; BAYES THEOREM; BINDING SITE; DNA MICROARRAY; DNA SEQUENCE; ESCHERICHIA COLI; GENE EXPRESSION; GENE TARGETING; GENETIC ALGORITHM; NONHUMAN; PRIORITY JOURNAL; STATISTICAL MODEL; TRANSCRIPTION REGULATION;

BACTERIA (MICROORGANISMS); ESCHERICHIA COLI;

EID: 33645107349     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/btk017     Document Type: Article

References (23)

1. Alter,O. et al. (2000) Singular value decomposition for genome-wide expression data processing and modeling. Proc. Natl Acad. Sci. USA, 97, 10101-10106.
2. Anderson,T. (1984) An Introduction to Multivariate Statistical Analysis. Wiley, New York.
3. Beal,M.J. et al. (2005) A Bayesian approach to reconstructing genetic regulatory networks with hidden factors. Bioinformatics, 21, 349-356.
4. Beer,M.A. and Tavazoie,S. (2004) Predicting gene expression from sequence. Cell, 117, 185-198.
5. Bussemaker,H.J. et al. (2001) Regulatory element detection using correlation with expression. Nat. Gene., 27, 167-171.
6. Conlon,E.M. et al. (2003) Integrating regulatory motif discovery and genome-wide expression analysis. Proc. Natl Acad. Sci. USA, 100, 3339-3344.
7. Courcelle,J. et al. (2001) Comparative gene expression profiles following UV exposure in wild-type and SOS-deficient Escherichia coli. Genetics, 158, 41-64.
8. Cowles,M.K. and Carlin,B.P. (1995) Markov Chain Monte Carlo diagnostics: A comparative review. J. Am. Stat. Soc, 91, 883-904.
9. Davidson,E.H. et al. (2002) A genomic regulatory network for development. Science, 295, 1669-1678.
10. Gardner,T.S. et al. (2003) Inferring genetics networks and identifying compound mode of action via expression profiling. Science, 301, 102-105.
11. Gao,F. et al. (2004) Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data. BMC Bioinformatics, 5, 31.
12. Girolami,M. and Breitling,R. (2004) Biologically valid linear factor models of gene expression. Bioinformatics, 20, 3021-3033.
13. Kao,K.C. et al. (2004) Transcriptome-based determination of multiple transcription regulator activities in Escherichia coli by using network component analysis. Proc. Natl Acad. Sci. USA, 101, 641-646.
14. Keles,S. et al. (2002) Identification of regulatory elements using a feature selection method. Bionformatics, 18, 1167-1175.
15. Khodursky,A.B. et al. (2000) DNA microarray analysis of gene expression in response to physiological and genetic changes that affect tryptophan metabolism in Escherichia coli. Proc. Natl Acad. Sci. USA, 97, 12170-12175.
16. Liao,J.C. et al. (2003) Network component analysis: Reconstruction of regulatory signals in biological systems. Proc. Natl Acad. Sci, USA, 100, 15522-15527.
17. Oh,M.K. and Liao,J.C. (2000a) Gene expression profiling by DNA microarrays and metabolic fluxes in Escherichia coli. Biotechnol. Prog., 16, 278-286.
18. Oh,M.K. and Liao,J.C. (2000b) DNA microarray detection of metabolic responses to protein overproduction in Escherichia coli. Metab. Eng., 2, 201-209.
19. Oh,M.-K. et al. (2002) Global expression profiling of acetate-grown Escherichia coli. J. Biol. Chem., 277, 13175-13183.
20. Sabatti,C. and Lange,K. (2002) Genomewise motif identification using a dictionary model. Proc. of IEEE, 90, 1803-1810.
21. Sabatti,C. et al. (2005) Vocabulon: A dictionary model approach for reconstruction and localization of transcription factor binding sites. Bioinformatics, 21, 922-931.
22. Segal,E. et al. (2003) Module networks: Identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet., 34, 166-176.
23. West,M. (2003) Bayesian factor regression models in the 'Large p, Small n' paradigm. Bayesian Stat., 7, 723-732.