An empirical Bayesian approach for model-based inference of cellular signaling networks

BMC Bioinformatics

Volumn 10, Issue , 2009, Pages

  Klinke, David J ^a,b  

^a West Virginia University   (United States)

^b WEST VIRGINIA UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE MARKOV CHAIN MONTE CARLO; CELLULAR SIGNALING NETWORKS; COMPLICATED STRUCTURES; CONVERGENCE CRITERION; EPIDERMAL GROWTH FACTORS; POSTERIOR DISTRIBUTIONS; PRIMARY RAT HEPATOCYTES; SIGNAL TRANSDUCTION MECHANISM;

FORECASTING; MATHEMATICAL MODELS; MONTE CARLO METHODS; SIGNAL TRANSDUCTION; SIGNALING;

BAYESIAN NETWORKS;

EPIDERMAL GROWTH FACTOR; EPIDERMAL GROWTH FACTOR RECEPTOR;

ARTICLE; BAYES THEOREM; CALIBRATION; LIVER CELL; MATHEMATICAL MODEL; MONTE CARLO METHOD; PROBABILITY; SIGNAL TRANSDUCTION; ANIMAL; ARTIFICIAL NEURAL NETWORK; BIOLOGY; CHEMISTRY; METABOLISM; METHODOLOGY; RAT;

RATTUS;

ANIMALS; BAYES THEOREM; COMPUTATIONAL BIOLOGY; EPIDERMAL GROWTH FACTOR; HEPATOCYTES; NEURAL NETWORKS (COMPUTER); RATS; SIGNAL TRANSDUCTION;

EID: 71049127915     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-10-371     Document Type: Article

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