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Clusternomics: Integrative context-dependent clustering for heterogeneous datasets
Integrative clustering is used to identify groups of samples by jointly analysing multiple datasets describing the same set of biological samples, such as gene expression, copy number, methylation etc. Most existing algorithms for integrative clustering assume that there is a shared consistent set o...
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| Vydáno v: | PLoS Comput Biol |
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| Hlavní autoři: | , , |
| Médium: | Artigo |
| Jazyk: | Inglês |
| Vydáno: |
Public Library of Science
2017
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| Témata: | |
| On-line přístup: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5658176/ https://ncbi.nlm.nih.gov/pubmed/29036190 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pcbi.1005781 |
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