An Iterative Locally Auto-Weighted Least Squares Method for Microarray Missing Value Estimation

• Impact Factor:3.206
• DOI number:10.1109/TNB.2016.2636243
• Affiliation of Author(s):西南交通大学
• Journal:IEEE Transactions on NanoBioscience
• Place of Publication:UNITED STATES
• Key Words:Auto-weighted local least squares, iterative estimation, microarray data analysis, 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.
• Co-author:Tianrui Li, Shi-Jinn Horng, Yi Pan, Hongjun Wang, Yunge Jing
• First Author:Zeng Yu
• Indexed by:Academic papers
• Document Code:20171603584461
• Discipline:Engineering
• First-Level Discipline:Computer Science and Technology
• Volume:Volume: 16
• Issue:Issue: 1, January 2017
• Page Number:21 - 33
• ISSN No.:1558-2639
• Translation or Not:no
• Date of Publication:2016-12-06