Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects

BACKGROUND: Time-course gene expression data such as yeast cell cycle data may be periodically expressed. To cluster such data, currently used Fourier series approximations of periodic gene expressions have been found not to be sufficiently adequate to model the complexity of the time-course data, p...

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Detalles Bibliográficos
Autores principales: Wang, Kui, Ng, Shu Kay, McLachlan, Geoffrey J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Methodology Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3574839/
https://www.ncbi.nlm.nih.gov/pubmed/23151154
http://dx.doi.org/10.1186/1471-2105-13-300

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3574839/
https://www.ncbi.nlm.nih.gov/pubmed/23151154
http://dx.doi.org/10.1186/1471-2105-13-300

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