A hybrid iterative approach for microarray missing value estimation

The completeness of gene expression data is essential to many gene expression data analysis issues. In this paper, inspired by the idea of semi-supervised learning with tri-training, a hybrid iterative imputation method called tri-imputation is proposed to estimate the missing values in gene expression data. In detail, in each round of tri-imputation, any two imputation methods are collaborating with each other to firstly estimate an initial imputation value, and then to be applied to the rest imputation method for providing different available information. Finally, all these three results are combined with their respective pre-trained confidence values. Experimental results on real microarray matrices indicate that tri-imputation achieves more accurate estimation for missing values in terms of the lowest normalized root-mean-square error.