Missing value imputation improves clustering and interpretation of gene expression microarray data
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1471-2105
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
Missing value imputation improves clustering and interpretation of gene expression microarray data ; volume:9 ; number:1 ; day:18 ; month:4 ; year:2008 ; pages:1-14 ; date:12.2008
BMC bioinformatics ; 9, Heft 1 (18.4.2008), 1-14, 12.2008
- Creator
-
Tuikkala, Johannes
- Contributor
-
Elo, Laura L.
Nevalainen, Olli S.
Aittokallio, Tero
SpringerLink (Online service)
- DOI
-
10.1186/1471-2105-9-202
- URN
-
urn:nbn:de:1111-201602173109
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:22 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Tuikkala, Johannes
- Elo, Laura L.
- Nevalainen, Olli S.
- Aittokallio, Tero
- SpringerLink (Online service)
Other Objects (12)
Accounting for one-channel depletion improves missing value imputation in 2-dye microarray data
Comparative analysis of missing value imputation methods to improve clustering and interpretation of microarray experiments
Robust imputation method for missing values in microarray data
Imputation Strategies for Clustering Mixed-Type Data with Missing Values
Improving missing value imputation of microarray data by using spot quality weights
Missing value imputation for microarray gene expression data using histone acetylation information
An efficient ensemble method for missing value imputation in microarray gene expression data
Missing Data, Imputation, and Endogeneity
Missing value imputation for microarray data: a comprehensive comparison study and a web tool
Missing value imputation for epistatic MAPs
Missing data imputation in clinical trials using recurrent neural network facilitated by clustering and oversampling
Fast accurate missing SNP genotype local imputation
Accounting for one-channel depletion improves missing value imputation in 2-dye microarray data
Comparative analysis of missing value imputation methods to improve clustering and interpretation of microarray experiments
Robust imputation method for missing values in microarray data
Imputation Strategies for Clustering Mixed-Type Data with Missing Values
Improving missing value imputation of microarray data by using spot quality weights
Missing value imputation for microarray gene expression data using histone acetylation information
An efficient ensemble method for missing value imputation in microarray gene expression data
Missing Data, Imputation, and Endogeneity
Missing value imputation for microarray data: a comprehensive comparison study and a web tool
Missing value imputation for epistatic MAPs
Missing data imputation in clinical trials using recurrent neural network facilitated by clustering and oversampling
Fast accurate missing SNP genotype local imputation
Accounting for one-channel depletion improves missing value imputation in 2-dye microarray data
Comparative analysis of missing value imputation methods to improve clustering and interpretation of microarray experiments
Robust imputation method for missing values in microarray data
Imputation Strategies for Clustering Mixed-Type Data with Missing Values
Improving missing value imputation of microarray data by using spot quality weights
Missing value imputation for microarray gene expression data using histone acetylation information
An efficient ensemble method for missing value imputation in microarray gene expression data
Missing Data, Imputation, and Endogeneity
Missing value imputation for microarray data: a comprehensive comparison study and a web tool
Missing value imputation for epistatic MAPs
Missing data imputation in clinical trials using recurrent neural network facilitated by clustering and oversampling