title: Sequential local least squares imputation estimating missing value of microarray data.
abstract: 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.
authors.affiliation: Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, PR China.
authors.forename: Xiaobai
authors.initials: X
authors.lastname: Zhang
authors.forename: Xiaofeng
authors.initials: X
authors.lastname: Song
authors.forename: Huinan
authors.initials: H
authors.lastname: Wang
authors.forename: Huanping
authors.initials: H
authors.lastname: Zhang
mesh_terms: D016018:Least-Squares Analysis; D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis