期刊
IEEE TRANSACTIONS ON NANOBIOSCIENCE
卷 16, 期 1, 页码 21-33出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNB.2016.2636243
关键词
Auto-weighted local least squares; iterative estimation; microarray data analysis; missing value estimation
资金
- National Science Foundation of China [61573292, 61572406]
- NSAF [U1230117]
- Scientific Research Foundation of Sichuan Provincial Education Department [13ZB0210]
Microarray data often contain missing values which significantly affect subsequent analysis. Existing LLSimpute-based imputation methods for dealing with missing data have been shown to be generally efficient. However, all of the LLSimpute-based methods do not consider the different importance of different neighbors of the target gene in the missing value estimation process and treat all the neighbors equally. In this paper, a locally auto-weighted least squares imputation (LAW-LSimpute) method is proposed for missing value estimation, which can automatically weight the neighboring genes based on the importance of the genes. Then, an accelerating strategy is added to the LAW-LSimpute method in order to improve the convergence. Furthermore, an iterative missing value estimation framework of LAW-LSimpute (ILAW-LSimpute) is designed. Experimental results show that the ILAW-LSimpute method is able to reduce the estimation error.
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