A Bayesian missing value estimation method for gene expression profile data

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Learning structure in gene expression data using deep architectures, with an application to gene clustering (2015, methodology): “Successful attempts include using a Bayesian method for imputation of missing values [9] and using a least squares based method [10].”
dbNSFP: A Lightweight Database of Human Non-synonymous SNPs and Their Functional Predictions (2011, methodology): “However, the distribution of the errors using BPCAfill had a significantly smaller standard deviation than the other strategy (0.303 comparing to 0.341, P-valueo10 14, F-test, one tailed).”
pcaMethods - a bioconductor package providing PCA methods for incomplete data (2007, background): “See Oba et al. (2003) for a detailed discussion of the BPCA algorithm.”
Imputation of missing data in life‐history trait datasets: which approach performs the best? (2014, methodology): “To assess the performance of each imputation approach, we calculated a Normalized Root Mean Squared Error [NRMSE (Oba et al. 2003)], which is the mean of squared imputation errors divided by the total”
MissForest - non-parametric missing value imputation for mixed-type data (2011, methodology): “After imputing the missing values the performance is assessed using the normalised root mean squared error (NRMSE, Oba et al. (2003)) for the continuous variables which is deﬁned by where X true is the complete data matrix and X imp the imputed data matrix.”