Statistical analysis with missing data
Statistical analysis with missing data
Predicting incomplete gene microarray data with the use of supervised learning algorithms
Pattern Recognition Letters
Missing value imputation in DNA microarrays based on conjugate gradient method
Computers in Biology and Medicine
WIMP: Web server tool for missing data imputation
Computer Methods and Programs in Biomedicine
Optimum estimation of missing values in randomized complete block design by genetic algorithm
Knowledge-Based Systems
Hi-index | 0.00 |
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.