A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters.

Gerstgrasser, Matthias; Nicholls, Sarah; Stout, Michael; Smart, Katherine; Powell, Chris; Kypraios, Theodore; Stekel, Dov

Gerstgrasser, Matthias; Nicholls, Sarah; Stout, Michael; Smart, Katherine; Powell, Chris; Kypraios, Theodore; Stekel, Dov.

Afiliação

Gerstgrasser M; * Department of Computer Science, University of Oxford, Parks Road, Oxford, OX1 3QD, UK.
Nicholls S; School of Biosciences, University of Nottingham, Sutton Bonington, Leicestershire, LE12 5RD, UK.
Stout M; School of Biosciences, University of Nottingham, Sutton Bonington, Leicestershire, LE12 5RD, UK.
Smart K; SAB Miller plc, Church Street West, Woking, GU21 6HS, UK.
Powell C; School of Biosciences, University of Nottingham, Sutton Bonington, Leicestershire, LE12 5RD, UK.
Kypraios T; § School of Mathematics, University of Nottingham, Nottingham, NG7 2RD, UK.
Stekel D; School of Biosciences, University of Nottingham, Sutton Bonington, Leicestershire, LE12 5RD, UK.

J Bioinform Comput Biol ; 14(3): 1650007, 2016 06.

Article em En | MEDLINE | ID: mdl-26762475

Assuntos

Biologia Computacional/métodos; Análise em Microsséries/métodos; Área Sob a Curva; Teorema de Bayes; Processos de Crescimento Celular; Cadeias de Markov; Método de Monte Carlo; Fenótipo

Palavras-chave

Bayesian statistics; Biolog; diauxic; growth model; lag phase; phenotype microarrays

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Biologia Computacional / Análise em Microsséries Tipo de estudo: Health_economic_evaluation / Prognostic_studies / Qualitative_research Idioma: En Ano de publicação: 2016 Tipo de documento: Article

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