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Missing Value Imputation in Gene Expression Data Using Histone Acetylation Information
XIANG Qian * #
School of Information Science and Technology, Sun Yat-Sen University, Guangzhou, 510275
*Correspondence author
#Submitted by
Subject:
Funding: Specialized Research Fund for the Doctoral Program of Higher Education（No.No. 4111279）
Opened online:23 December 2010
Accepted by: none
Citation: XIANG Qian.Missing Value Imputation in Gene Expression Data Using Histone Acetylation Information[OL]. [23 December 2010] http://en.paper.edu.cn/en_releasepaper/content/4398573
 
 
Accurate estimation of missing values in microarray data is important for the expression profile analysis. In this paper, missing value imputation is done with the aid of gene regulatory mechanism. It incorporates histone acetylation into the conventional k-nearest neighbor and local least square imputation algorithms for final prediction. The comparison results indicated that the proposed method consistently improves the widely used methods and outperforms GOimpute in terms of normalized root mean squared error(NRMSE), which is one of the existing related methods that use the functional similarity as the external information. The results demonstrated histone acetylation information may be more highly correlated with the gene expression than that of functional similarity.
Keywords:Bioinformatics; missing valuet; gene expression; histone acetylation
 
 
 

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