Clustering column-mean quantile median: a new methodology for imputing missing data

Abstract

Abstract DNA microarray data sets have been widely explored and used to analyze data without any previous biological background. However, analyzing them becomes challenging if data are missing. Thus, machine learning techniques are applied because microarray technology is promising in genomics, especially in the analysis of gene expression data. Furthermore, gene expression data can describe the transcription and translation processes of each genetic information in detail. In this study, a new system was proposed to impute more realizable values for missing data in a microarray dataset. This system was validated and evaluated on 42 samples of rectal cancer. Several evaluation tests were also conducted to confirm the effectiveness of the new system and compare it with highly known imputing algorithms. The proposed clustering column-mean quantile median technique could predict highly informative missing genes, thereby reducing the difference between the original and imputed datasets and demonstrating its efficiency.

Keywords

• Microarray
• Missing data
• Imputation
• Machine learning