Communications of the Korean Institute of Information Scientists and Engineers (정보과학회지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지

서열 모티프 발굴을 위한 확률 그래프 모델

  • Published : 2011.02.28
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Abstract

Keywords

Acknowledgement

Supported by : 한국연구재단

References

  1. Brivanlou, A.H. and Darnell, J.E., Jr., "Signal transduction and the control of gene expression", Science, 295, 813-818, 2002. https://doi.org/10.1126/science.1066355
  2. Emerson, B.M., "Specificity of gene regulation", Cell, 109, 267-270, 2002. https://doi.org/10.1016/S0092-8674(02)00740-7
  3. Wasserman, W.W. and Sandelin, A., "Applied bioinformatics for the identification of regulatory elements", Nat. Rev. Genet., 5, 276-287, 2004. https://doi.org/10.1038/nrg1315
  4. Hawkins, R.D., Hon, G.C. and Ren, B., "Next-generation genomics: an integrative approach", Nat. Rev. Genet., 11, 476-486, 2010. https://doi.org/10.1038/nrg2795
  5. Bishop, C.M., "Pattern recognition and machine learning", Springer, 2006.
  6. Jordan, M.I., "An introduction to probabilistic graphical models", in preparation
  7. Lauritzenp, S.L., "Graphical models", Oxford University Press, 1996.
  8. Hertz, G.Z., Hartzell 3rd, G.W., and Stormo, G.D., "Identication of consensus patterns in unaligned DNA sequences known to be functionally related". Comput. Appl. Biosci., 6, 81-92, 1990.
  9. Lawrence, C.E. and Reilly, A.A., "An expectation maximization (EM) algorithm for the identication and characterization of common sites in unaligned biopolymer sequences", Proteins, 7, 41-51, 1990. https://doi.org/10.1002/prot.340070105
  10. Lawrence, C.E., Altschul, S.F., Boguski, M.S., Liu, J.S., Neuwald, A.F., and Wootton, J.C., "Detecting subtle sequence signals: a Gibbs sampling strategy for multiple alignment", Science, 262, 208-214, 1993. https://doi.org/10.1126/science.8211139
  11. Bailey, T.L., and Elkan, C., "The value of prior knowledge in discovering motifs with MEME", International Conference Intelligent Systems for Molecular Biology, 1995.
  12. Narlikar, L., Gordan, R., Ohler, U., and Hartemink, A. J., "Informative priors based on transcription factor structural class improve de novo motif discovery", Bioinformatics, 22, e384-e392, 2006. https://doi.org/10.1093/bioinformatics/btl251
  13. Bailey, T.L., and Elkan, C., "Fitting a mixture model by expectation maximization to discover motifs in biopolymers", International Conference Intelligent Systems for Molecular Biology, 1994.
  14. Liu, J.S., Neuwald, A.F., and Lawrence, C.E., "Bayesian models for multiple local sequence alignment and Gibbs sampling strategies", J. Amer. Statist. Assoc., 90, 1156-1170, 1995. https://doi.org/10.1080/01621459.1995.10476622
  15. Segal, E., Barash, Y., Simon, I., Friedman, N., and Koller, D., "From promoter sequence to expression: a probabilistic framework", International Conference on Research in Computational Molecular Biology, 2002.
  16. Redhead, E. and Bailey, T.L., "Discriminative motif discovery in DNA and protein sequences using the DEME algorithm", BMC Bioinformatics, 8, 385, 2007. https://doi.org/10.1186/1471-2105-8-385
  17. Smith, A.D., Sumazin, P., and Zhang, M.Q. "Identifying tissue selective transcription factor binding sites in vertebrate promoters", Proc. Natl Acad. Sci. USA, 102, 1560-1565, 2005. https://doi.org/10.1073/pnas.0406123102
  18. Sinha, S., "On counting position weight matrix matches in a sequence, with application to discriminative motif finding", Bioinformatics, 22, e454-e463, 2006. https://doi.org/10.1093/bioinformatics/btl227
  19. Kim, J.K., and Choi, S., "Probabilistic models for semi-supervised discriminative motif discovery in DNA sequences", IEEE/ACM Transactions on Computational Biology and Bioinformatics, in press
  20. Farnham, P.J., "Insights from genomic profiling of transcription factors", Nat. Rev. Genet., 10, 605-616, 2009. https://doi.org/10.1038/nrg2636
  21. Hollenhorst, P.C., Chandler, K.J., Poulsen, R.L., Johnson, W.E., Speck, N.A., and Graves, B.J., "DNA specificity determinants associate with distinct transcription factor functions", PLoS Genet., 5, e1000778, 2009. https://doi.org/10.1371/journal.pgen.1000778
  22. Badis, G., M.F. Berger, Philippakis, A.A., Talukder, S., Gehrke, A.R., Jaeger, S.A., Chan, E.T., Metzler, G., Vedenko, A., Chen, X., Kuznetsov, H., Wang, C.F., Coburn, D., Newburger, D.E., Morris, Q., Hughes, T. R., and Bulyk, M.L., "Diversity and complexity in DNA recognition by transcription factors", Science, 324, 1720-1723, 2009. https://doi.org/10.1126/science.1162327
  23. Kim, J.K., and Choi, S., "Clustering sequence sets for motif discovery", Advances in Neural Information Processing Systems, 2009.