Impact of missing data imputation methods on gene expression clustering and classification

Missing value imputation for microarray data: a comprehensive comparison study and a web tool

(2013) Chia-Chun Chiu et al.   BMC Systems Biology

On the choice of the best imputation methods for missing values considering three groups of classification methods

(2011) Julián Luengo et al.   KNOWLEDGE AND INFORMATION SYSTEMS

Analysis of complexity indices for classification problems: Cancer gene expression data

(2011) Ana C. Lorena et al.   NEUROCOMPUTING

Biological impact of missing-value imputation on downstream analyses of gene expression profiles

(2010) Sunghee Oh et al.   BIOINFORMATICS

Comparative analysis of missing value imputation methods to improve clustering and interpretation of microarray experiments

(2010) Magalie Celton et al.   BMC GENOMICS

Missing value imputation for gene expression data: computational techniques to recover missing data from available information

(2010) A. W.-C. Liew et al.   BRIEFINGS IN BIOINFORMATICS

Dealing with missing values in large-scale studies: microarray data imputation and beyond

(2009) T. Aittokallio   BRIEFINGS IN BIOINFORMATICS

Clustering cancer gene expression data: a comparative study

(2008) Marcilio C. P. de Souto et al.   BMC BIOINFORMATICS

Missing value imputation improves clustering and interpretation of gene expression microarray data

(2008) Johannes Tuikkala et al.   BMC BIOINFORMATICS