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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Detalles Bibliográficos
Autores principales: Gabasova, Evelina, Reid, John, Wernisch, Lorenz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5658176/
https://www.ncbi.nlm.nih.gov/pubmed/29036190
http://dx.doi.org/10.1371/journal.pcbi.1005781

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5658176/
https://www.ncbi.nlm.nih.gov/pubmed/29036190
http://dx.doi.org/10.1371/journal.pcbi.1005781

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