Cargando…
Microarray missing data imputation based on a set theoretic framework and biological knowledge
Gene expressions measured using microarrays usually suffer from the missing value problem. However, in many data analysis methods, a complete data matrix is required. Although existing missing value imputation algorithms have shown good performance to deal with missing values, they also have their l...
Autores principales: | Gan, Xiangchao, Liew, Alan Wee-Chung, Yan, Hong |
---|---|
Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2006
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1409680/ https://www.ncbi.nlm.nih.gov/pubmed/16549873 http://dx.doi.org/10.1093/nar/gkl047 |
Ejemplares similares
-
Robust imputation method for missing values in microarray data
por: Yoon, Dankyu, et al.
Publicado: (2007) -
Discovering biclusters in gene expression data based on high-dimensional linear geometries
por: Gan, Xiangchao, et al.
Publicado: (2008) -
A new analytical framework for missing data imputation and classification with uncertainty: Missing data imputation and heart failure readmission prediction
por: Hu, Zhiyong, et al.
Publicado: (2020) -
Improving missing value imputation of microarray data by using spot quality weights
por: Johansson, Peter, et al.
Publicado: (2006) -
Missing value imputation improves clustering and interpretation of gene expression microarray data
por: Tuikkala, Johannes, et al.
Publicado: (2008)