Multiple sequence local alignment using Monte Carlo em algorithm

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4463 LNBI, Issue , 2007, Pages 465-476

  Bi, Chengpeng ^a  

^a UNIVERSITY OF MISSOURI KANSAS CITY SCHOOL OF MEDICINE   (United States)

Author keywords

Expectation Maximization (EM); Monte Carlo EM; Motif discovery; Multiple sequence local alignment; Transcriptional regulation

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; MATHEMATICAL MODELS;

EXPECTATION MAXIMIZATION (EM); MONTE CARLO EM MOTIF DISCOVERY ALGORITHM (MCEMDA); MOTIF DISCOVERY; MULTIPLE SEQUENCE LOCAL ALIGNMENT; TRANSCRIPTIONAL REGULATION;

MONTE CARLO METHODS;

EID: 34547433608     PISSN: 03029743     EISSN: 16113349     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-72031-7_42     Document Type: Conference Paper

References (23)

1. MacIsaac, K.D. and Fraenkel, E.: Practical Strategies for Discovering Regulatory DNA Sequence Motifs. PLoS Comput Biol, 2 (2006) e36
2. Tompa, M. et al.: Assessing Computational Tools for the Discovery of Transcription Factor Binding Sites. Nature Biotechnology 23 (2005) 137-144
3. Lawrence, CE. and Reilly, A.A.: An Expectation Maximization Algorithm for the Identification and Characterization of Common Sites in Unaligned Biopolymer Sequences. Proteins: Structure, Function and Genetics 7 (1990) 41-51
4. Dempster, A.P. et al.: Maximum Likelihood from Incomplete Data via the EM Algorithm (with Discussion). J. the Royal Statist. Soc. B 39 (1977) 1-38
5. Bailey, T.L. and Elkan, C.: Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization. Machine Learning 21 (1995) 51-80
6. Celeux, G et al.: Stochastic Versions of the EM Algorithm: An Experimental Study in the Mixture Case. J. Statist. Comput. Simul. 55 (1996) 287-314
7. Wei, G.C.G. and Tanner, M.A.: A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms. Journal of the American Statistical Association 85 (1990) 699-704
8. Delyon, B. et al.: Convergence of a Stochastic Approximation Version of the EM Algorithm. Ann. Statist. 27 (1999) 94-128
9. Berg, O.G. and von Hippel, P.H.: Selection of DNA Binding Sites by Regulatory Proteins: Statistical-mechanical Theory and Application to Operators and Promoters. Journal of Molecular Biology 193 (1987) 723-750
10. Bonizzoni, P. and Vedova, G.D.: The Complexity of Multiple Sequence Alignment with SP-score That Is a Metric. Theoretical Computer Science 259 (2001) 6379
11. Bi, C-P.: SEAM: A Stochastic EM-type Algorithm for Motif-Finding in Biopolymer Sequences. J. Bioinformatics and Comput. Biol. (2007) in press
12. Wu, C.F.J.: On the Convergence Properties of the EM Algorithm. The Annals of Statistics 11 (1983) 95-103
13. Lawrence, CE. et al.: Detecting Subtle Sequence Signals: A Gibbs Sampling Strategy for Multiple Alignment. Science 262 (1993) 208-214
14. Liu, X. et al.: BioProspector: Discovering Conserved DNA Motifs in Upstream Regulatory Regions of Co-expressed Genes. Pacific Symposium on Biocomputing 6 (2001) 127-138
15. Schneider, T.D. and Stephens, R.M.: Sequence Logos: A New Way to Display Consensus Sequences. Nucleic Acids Research 18 (1990) 6097-6100.
16. Crooks, G.E. et al.: WebLogo: A Sequence Logo Generator. Genome Research 14 (2004) 1188-1190
17. Salgado, H. et al.: RegulonDB (version 5.0): Escherichia coli K-12 Transcriptional Regulatory Network, Operon Organization, and Growth Conditions. Nucleic Acids Res. 34 (2006) D394-7
18. Kel, A.E. et al.: Computer-assisted Identification of Cell Cycle-related Genes: New Targets for E2F Transcription Factors. J. Mol. Biol. 309 (2001) 99-120
19. Klinge, C.M.: Estrogen Receptor Interaction with Estrogen Response Elements. Nucleic Acids Res. 29 (2001) 2905-2919
20. Wei, Z. and Jensen, S.T.: GAME: Detecting cis-Regulatory Elements Using a Genetic Algorithm. Bioinformatics 22 (2006) 1577-1584
21. Martnez-Buenol, M. et al.: BacTregulators: A Database of Transcriptional Regulators in Bacteria and Archaea. Bioinformatics 20 (2004) 2787-2791
22. Krell, T. et al.: The IclR Family of Transcriptional Activators and Repressors Can Be Defined by a Single Profile. Protein Science 15 (2006) 1207-1213
23. Bi, C-P.: A Genetic-Based EM Motif-Finding Algorithm for Biological Sequence Analysis. Proceeding of IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (2007) in press