期刊
COMPUTERS IN BIOLOGY AND MEDICINE
卷 38, 期 10, 页码 1112-1120出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2008.08.006
关键词
Missing value estimation; Imputation method; Least squares principle; Normalized root mean squared error (NRMSE); Microarray data
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.
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