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Predicting Gene Structure with the Use of Mixtures of Probability Distributions

  • Systems Analysis
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Cybernetics and Systems Analysis Aims and scope

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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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

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