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Interpolation based consensus clustering for gene expression time series
BACKGROUND: Unsupervised analyses such as clustering are the essential tools required to interpret time-series expression data from microarrays. Several clustering algorithms have been developed to analyze gene expression data. Early methods such as k-means, hierarchical clustering, and self-organiz...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2015
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4407314/ https://www.ncbi.nlm.nih.gov/pubmed/25888019 http://dx.doi.org/10.1186/s12859-015-0541-0 |