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学历:博士研究生毕业
学位:工学博士学位
办公地点:犀浦3号教学楼31529
毕业院校:四川大学
学科:电子信息. 软件工程. 计算机应用技术
所在单位:计算机与人工智能学院
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An Iterative Locally Auto-Weighted Least Squares Method for Microarray Missing Value Estimation
影响因子:3.206
DOI码:10.1109/TNB.2016.2636243
所属单位:西南交通大学
发表刊物:IEEE Transactions on NanoBioscience
刊物所在地:UNITED STATES
关键字:Auto-weighted local least squares, iterative estimation, microarray data analysis, missing value estimation
摘要: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.
合写作者:李天瑞, Shi-Jinn Horng, Yi Pan, Hongjun Wang, Yunge Jing
第一作者:Zeng Yu
论文类型:学术论文
论文编号:20171603584461
学科门类:工学
一级学科:计算机科学与技术
卷号:Volume: 16
期号:Issue: 1, January 2017
页面范围:21 - 33
ISSN号:1558-2639
是否译文:否
发表时间:2016-12-06