Missing Value Estimation for Gene Expression Profile Data  

作　　者：WANG Xuesong LIU Qingfeng CHENG Yuhu 

机构地区：[1]School of Information and Electrical Engineering,China University of Mining and Technology,Xuzhou 221116,China

出　　处：《Chinese Journal of Electronics》2012年第4期673-677,共5页电子学报（英文版）

基　　金：This work is supported by the National Natural Science Foundation of China （No.60804022, No.60974050, No.61072094）, Program for New Century Excellent Talents in University （No.NCET-08-0836）, Fok Ying-Tung Education Foundation for Young Teachers （No.121066）, Natural Science Foundation of Jiangsu Province （No.BK2008126）.

摘　　要：A new Missing value （MV） estimation method for gene expression profile data is proposed by considering both the internal and external conditions of gene expression profiles. The internal condition emphasizes the time-series characteristic of gene expression profile data. Therefore, we can use the cubic spline fitting method to construct a gene expression curve so as to estimate Mrs. The main idea of MV estimation based on the external condition is to reconstruct MVs according to the expression values of candidate genes. Firstly, an initial subset of candidate genes is determined by defining a trace matrix. Then a final subset of candidate genes is constructed by selecting genes from the initial subset according to an improved Pearson correlation coefficient. At last, we select K genes that are most correlated with the target gene from the final subset to compute the weighted sum of the K expres- sion values. Thus, the weighted sum is the estimated value of the target gene based on the external condition. Experimental results indicate that, compared with commonly used MY estimation methods, KNNimpute, SKNNimpute and IKNNimpute, the proposed method has higher esti- mation accuracy and is robust to the magnitude of K.

关 键 词：Gene expression profile Missing values Correlation coefficient Curve fitting Trace matrix. 

分 类 号：O241.6[理学—计算数学] Q786[理学—数学]