Sequential local least squares imputation estimating missing value of microarray data

被引：49
|
作者：
Zhang, Xiaobai [1 ]
Song, Xiaofeng [1 ]
Wang, Huinan [1 ]
Zhan, Huanping [1 ]
机构：
[1] Nanjing Univ Aeronaut & Astronaut, Dept Biomed Engn, Nanjing 210016, Peoples R China
来源：
COMPUTERS IN BIOLOGY AND MEDICINE | 2008年 / 38卷 / 10期
关键词：
Missing value estimation; Imputation method; Least squares principle; Normalized root mean squared error (NRMSE); Microarray data;
D O I：
10.1016/j.compbiomed.2008.08.006
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
Missing values in microarray data can significantly affect subsequent analysis, thus it is important to estimate these missing values accurately. In this paper, a sequential local least squares imputation (SLLSimpute) method is proposed to solve this problem. It estimates missing values sequentially from the gene containing the fewest missing values and partially utilizes these estimated values. In addition, an automatic parameter selection algorithm, which can generate an appropriate number of neighboring genes for each target gene, is presented for parameter estimation. Experimental results confirmed that SLLSimpute method exhibited better estimation ability compared with other currently used imputation methods. (C) 2008 Elsevier Ltd. All rights reserved.
引用
收藏
页码：1112 / 1120
页数：9
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