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Clustering of temporal gene expression data with mixtures of mixed effects models with a penalized likelihood
MOTIVATION: Clustering algorithms like K-Means and standard Gaussian mixture models (GMM) fail to account for the structure of variability of replicated data or repeated measures over time. Additionally, a priori cluster number assumptions add an additional complexity to the process. Current methods...
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| Publicado en: | Bioinformatics |
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| Autores principales: | , , , |
| Formato: | Artigo |
| Lenguaje: | Inglês |
| Publicado: |
Oxford University Press
2019
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| Materias: | |
| Acceso en línea: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6394398/ https://ncbi.nlm.nih.gov/pubmed/30101356 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/bioinformatics/bty696 |
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