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

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

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 ac...

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Veröffentlicht in:Cybernetics and systems analysis 2007-03, Vol.43 (2), p.208-212
Hauptverfasser: Sergienko, I V, Beletskii, B A, Vasil'ev, S V, Gupal, A M
Format: Artikel
Sprache:eng
Schlagworte:
Accuracy
Algorithms
Amino acids
Bayesian analysis
Classification
Cybernetics
Energy
Markov analysis
Protein folding
Proteins
Studies
Systems analysis
Traveling salesman problem
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Zusammenfassung: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. [PUBLICATION ABSTRACT]
ISSN:1060-0396
1573-8337
DOI:10.1007/s10559-007-0039-5