Cargando…
CLIC: clustering analysis of large microarray datasets with individual dimension-based clustering
Large microarray data sets have recently become common. However, most available clustering methods do not easily handle large microarray data sets due to their very large computational complexity and memory requirements. Furthermore, typical clustering methods construct oversimplified clusters that...
Autores principales: | Yun, Taegyun, Hwang, Taeho, Cha, Kihoon, Yi, Gwan-Su |
---|---|
Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2010
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2896182/ https://www.ncbi.nlm.nih.gov/pubmed/20529873 http://dx.doi.org/10.1093/nar/gkq516 |
Ejemplares similares
-
FiGS: a filter-based gene selection workbench for microarray data
por: Hwang, Taeho, et al.
Publicado: (2010) -
Biclustering for the comprehensive search of correlated gene expression patterns using clustered seed expansion
por: Yun, Taegyun, et al.
Publicado: (2013) -
Discovering transnosological molecular basis of human brain diseases using biclustering analysis of integrated gene expression data
por: Cha, Kihoon, et al.
Publicado: (2015) -
Disease association and inter-connectivity analysis of human brain specific co-expressed functional modules
por: Oh, Kimin, et al.
Publicado: (2015) -
Discovering gene expression signatures responding to tyrosine kinase inhibitor treatment in chronic myeloid leukemia
por: Cha, Kihoon, et al.
Publicado: (2016)