Gene expression microarray experiments frequently generate data sets with multiple values missing. Unfortunately, most of the analysis, mining and classification methods for gene expression data require a complete matrix of gene array values. Therefore, the accurate estimation of missing values in such data sets has been recognized as an important issue and several imputation algorithms have already been proposed to the biological community. However, most of these approaches are not particularly suitable for time series expression profiles.

DTWimpute ⁽¹⁾ is a C++ implementation of a two-pass imputation algorithm, based on Dynamic Time Warping (DTW) distance, which is specially suited for estimation of missing values in gene expression time series data.