Cargando…
Network-based de-noising improves prediction from microarray data
BACKGROUND: Prediction of human cell response to anti-cancer drugs (compounds) from microarray data is a challenging problem, due to the noise properties of microarrays as well as the high variance of living cell responses to drugs. Hence there is a strong need for more practical and robust methods...
Autores principales: | Kato, Tsuyoshi, Murata, Yukio, Miura, Koh, Asai, Kiyoshi, Horton, Paul B, Tsuda, Koji, Fujibuchi, Wataru |
---|---|
Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2006
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1810315/ https://www.ncbi.nlm.nih.gov/pubmed/16723007 http://dx.doi.org/10.1186/1471-2105-7-S1-S4 |
Ejemplares similares
-
Classification of heterogeneous microarray data by maximum entropy kernel
por: Fujibuchi, Wataru, et al.
Publicado: (2007) -
Embracing noise to improve cross-batch prediction accuracy
por: Koh, Chuan Hock, et al.
Publicado: (2012) -
Improving the Performance of SVM-RFE to Select Genes in Microarray Data
por: Ding, Yuanyuan, et al.
Publicado: (2006) -
Integrated analysis of the heterogeneous microarray data
por: Yi, Sung Gon, et al.
Publicado: (2011) -
Mixture modeling of microarray gene expression data
por: Yang, Yang, et al.
Publicado: (2007)