title: An Iterative Locally Auto-Weighted Least Squares Method for Microarray Missing Value Estimation.
abstract: 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.
authors.forename: Zeng
authors.initials: Z
authors.lastname: Yu
authors.forename: Tianrui
authors.initials: T
authors.lastname: Li
authors.forename: Shi-Jinn
authors.initials: SJ
authors.lastname: Horng
authors.forename: Yi
authors.initials: Y
authors.lastname: Pan
authors.forename: Hongjun
authors.initials: H
authors.lastname: Wang
authors.forename: Yunge
authors.initials: Y
authors.lastname: Jing
mesh_terms: D000465:Algorithms; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D016018:Least-Squares Analysis; D008223:Lymphoma / Q000235:genetics; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*