Paper Info
Title
Triple imputation for microarray missing value estimation
Abstract
Data obtained from gene expression microarray experiments always suffer from missing values due to various reasons. However, complete gene expression data are of great importance to many gene expression data analysis issues. Therefore, imputation methods with high estimation precision are critical to further data analysis. In this paper, inspired by the idea of semi-supervised learning with tri-training, we propose a novel imputation method called TRIIM (TRIple IMputation). TRIIM estimates missing values using triple imputation strategies based on Bayesian principal component analysis (BPCA), local least squares (LLS) and expectation maximization (EM). The data properties of global correlation information, local structure and data distribution are all considered properly. It is implemented by sharing the estimated values of any two algorithms' cooperation to the rest at each step, and assembling combinations of all imputation results finally. Experimental results on four real microarray matrices demonstrate that TRIIM achieves better performance than the comparative algorithms in terms of normalized root mean square error (NRMSE), even in the case of microarray dataset with large missing rates and few complete genes.
Year
DOI
Venue
2015
10.1109/BIBM.2015.7359682
IEEE International Conference on Bioinformatics and Biomedicine
Keywords
Field
DocType
microarray gene expression data, missing value imputation, semi-supervised learning
Least squares,Data mining,Monad (category theory),Semi-supervised learning,Pattern recognition,Computer science,Matrix (mathematics),Expectation–maximization algorithm,Correlation,Artificial intelligence,Missing data,Imputation (statistics)
Conference
ISSN
Citations 
PageRank 
2156-1125
1
0.35
References 
Authors
12
5
Name
Order
Citations
PageRank
Chong He110.35
Hui-Hui Li220.71
Changbo Zhao331.40
Guo-Zheng Li436842.62
Wei Zhang51221180.16