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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...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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BioMed Central
2012
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| 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 |