Improving Missing Value Imputation in Microarray Data by Using Gene Regulatory Information
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.
missing valuet gene ezpression histone acetylation
Qian Xiang Xianhua Dai
Department of Electronics & Communications Engineering Sun Yat-Sen University Guangzhou, P.R.China
国际会议
上海
英文
326-329
2008-05-16（万方平台首次上网日期，不代表论文的发表时间）