Missing value estimation for gene expression data based on Mahalanobis distance
基于马氏距离的缺失值填充算法

A imputation method based on Mahalanobis distance was proposed to estimate missing values in the gene expression data. The nearest neighbors were chosen by the Mahalanobis distance between genes, and then the concept of entropy was utilized to obtain estimations of missing values. The imputed values were used for the later imputation. Experiments prove that the method is valid and its performance is higher than the other imputation methods based on k-nearest neighbors for gene expression data.