Applying statistical criteria to choose optimal metaparameters in gene fragment recognition

DOI10.1007/s10559-016-9804-7zbMath1343.60107OpenAlexW2317423737MaRDI QIDQ289802

Alexey Ostrovsky

Publication date: 31 May 2016

Published in: Cybernetics and Systems Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10559-016-9804-7

zbMATH Keywords

likelihood ratio testnucleotideergodicityMarkov modelsBayesian mixturesexongene fragment recognitionhidden stateintronoptimal metaparameters

Mathematics Subject Classification ID

Parametric hypothesis testing (62F03) Bayesian inference (62F15) Markov chains (discrete-time Markov processes on discrete state spaces) (60J10) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Stochastic systems in control theory (general) (93E03) Genetics and epigenetics (92D10)

Uses Software

• GenBank

Cites Work

• Using EM-algorithm for gene classification
• Determination of the order of a Markov chain by Akaike's information criterion
• The $\chi^2$ Test of Goodness of Fit
• A new look at the statistical model identification

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