Predicting protein secondary structure based on Bayesian classification procedures on Markovian chains

Authors:
I. V. Sergienko;B. A. Beletskii;S. V. Vasil'Ev;A. M. Gupal
Affiliations:
V. M. Glushkov Institute of Cybernetics, National Academy of Sciences of Ukraine, Kyiv, Ukraine;V. M. Glushkov Institute of Cybernetics, National Academy of Sciences of Ukraine, Kyiv, Ukraine;V. M. Glushkov Institute of Cybernetics, National Academy of Sciences of Ukraine, Kyiv, Ukraine;V. M. Glushkov Institute of Cybernetics, National Academy of Sciences of Ukraine, Kyiv, Ukraine
Venue:
Cybernetics and Systems Analysis
Year:
2007

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Abstract

The paper discusses numerical results of predicting protein secondary structure using Bayesian classification procedures based on nonstationary Markovian chains. A new approach is used, based on the classification of pairs of states for pairs of neighboring amino acids. It improves the prediction accuracy as compared with that of the classification of the state of one amino acid.