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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...
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| Published in: | BMC Bioinformatics |
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| Main Authors: | , , , |
| Format: | Artigo |
| Language: | Inglês |
| Published: |
BioMed Central
2015
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| Subjects: | |
| Online Access: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4407314/ https://ncbi.nlm.nih.gov/pubmed/25888019 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12859-015-0541-0 |
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