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Predicting Gene Structure with the Use of Mixtures of Probability Distributions

Cybernetics and Systems Analysis volume 51, pages 361–369 (2015)Cite this article

Abstract

The authors consider the problem of reconstruction of hidden state sequences for mixture distributions with constituents described by the generalization of high-order Markov chains and hidden Markov models. A new algorithm to solve the problem using dynamic programming is proposed, as well as its modifications to eliminate recursion and reduce search. The results are applied to the problem of gene fragment recognition in plants.

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References

  1. K. Knapp and Y.-P. P. Chen, “An evaluation of contemporary hidden Markov model genefinders with a predicted exon taxonomy,” Nucleic Acids Research, 35, 317–324 (2007).

    Article  Google Scholar 

  2. I. V. Sergienko, A. M. Gupal, and A. V. Ostrovsky, “Recognition of DNA gene fragments using hidden Markov models,” Cybern. Syst. Analysis, 48, No. 3, 369–377 (2012).

    MathSciNet  Article  Google Scholar 

  3. A. M. Gupal and A. V. Ostrovsky, “Using compositions of Markov models to determine functional gene fragments,” Cybern. Syst. Analysis, 49, No. 5, 692–698 (2013).

    MATH  MathSciNet  Article  Google Scholar 

  4. I. V. Sergienko, A. M. Gupal, and A. V. Ostrovskiy, “Using EM-algorithm for gene classification,” Cybern. Syst. Analysis, 51, No. 1, 41–50 (2015).

    Article  Google Scholar 

  5. A. V. Ostrovskiy, “Detecting the proteins secondary structure using Markov models,” J. Autom. Inform. Sci., 45, No. 3, 75–83 (2013).

    Article  Google Scholar 

  6. The National Center for Biotechnology Information of the USA, http://ncbi.nlm.nih.gov/.

  7. A. Y. Ng, “Preventing overfitting of cross-validation data,” in: Proc. 14th Intern. Conf. on Machine Learning, Morgan Kaufmann, Waltham (1997), pp. 245–253.

    Google Scholar 

  8. I. V. Sergienko, B. A. Beletskii, S. V. Vasil’ev, and A. M. Gupal, “Predicting protein secondary structure based on Bayesian classification procedures on Markovian chains,” Cybern. Syst. Analysis, 43, No. 2, 208–212 (2007).

    MATH  Article  Google Scholar 

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Authors and Affiliations

  1. V. M. Glushkov Institute of Cybernetics, National Academy of Sciences of Ukraine, Kyiv, Ukraine

    I. V. Sergienko, A. M. Gupal & A. V. Ostrovskiy

Authors
  1. I. V. Sergienko
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  2. A. M. Gupal
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  3. A. V. Ostrovskiy
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Corresponding author

Correspondence to I. V. Sergienko.

Additional information

Translated from Kibernetika i Sistemnyi Analiz, No. 3, May–June, 2015, pp. 44–53.

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Sergienko, I.V., Gupal, A.M. & Ostrovskiy, A.V. Predicting Gene Structure with the Use of Mixtures of Probability Distributions. Cybern Syst Anal 51, 361–369 (2015). https://doi.org/10.1007/s10559-015-9728-7

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  • DOI: https://doi.org/10.1007/s10559-015-9728-7

Keywords

  • Markov chain
  • hidden variables
  • gene
  • bioinformatics
  • nucleotide
  • exon
  • intron
  • likelihood