A Bayesian autoregressive three-state hidden Markov model for identifying switching monotonic regimes in Microarray time course data

Statistical Applications in Genetics and Molecular Biology

Volumn 11, Issue 4, 2012, Pages

  Farcomeni, Alessio ^a   Arima, Serena ^a  

^a SAPIENZA UNIVERSITY OF ROME   (Italy)

Author keywords

Bayesian statistics; HMM; MCMC; Microarray time course data

Indexed keywords

TRANSCRIPTION FACTOR FOXC2;

ALGORITHM; ARTICLE; BAYES THEOREM; BAYESIAN LEARNING; CHROMOSOME DELETION 22Q11; DATA ANALYSIS; DNA MICROARRAY; EMBRYO DEVELOPMENT; GENE EXPRESSION; GENETIC MODEL; HIDDEN MARKOV MODEL; HISTOGRAM; HUMAN; MAXIMUM LIKELIHOOD METHOD; MEASUREMENT ERROR; MESENCHYME CELL; NONHUMAN; POLYMERASE CHAIN REACTION; PROBABILITY; REGULATOR GENE; SIMULATION; STANDARDIZATION; TIME SERIES ANALYSIS; ANIMAL; BIOLOGICAL MODEL; DIGEORGE SYNDROME; GENETICS; METHODOLOGY; MICROARRAY ANALYSIS; MOUSE; PRENATAL DEVELOPMENT; STATISTICAL MODEL; VELOCARDIOFACIAL SYNDROME;

MUS;

ALGORITHMS; ANIMALS; BAYES THEOREM; DIGEORGE SYNDROME; EMBRYONIC DEVELOPMENT; HUMANS; MARKOV CHAINS; MICE; MICROARRAY ANALYSIS; MODELS, GENETIC; MODELS, STATISTICAL;

EID: 84866453483     PISSN: None     EISSN: 15446115     Source Type: Journal    
DOI: 10.1515/1544-6115.1778     Document Type: Article

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