Use of biclustering for missing value imputation in gene expression data
The gene expression data shows the expression values of ten thousands of genes under hundreds of experimental conditions [1]. The data is useful for various applications such as cellular processes analysis, gene functions prediction and diseases diagnoses [2, 3]. However, some values in the gene expression data are missing due to image corruption, dust or […]