Missing-values imputation algorithms for microarray gene expression data

In gene expression studies, missing values are a common problem with important consequences for the interpretation of the final data (Satija et al., Nat Biotechnol 33(5):495, 2015). Numerous bioinformatics examination tools are used for cancer prediction, including the data set matrix (Bailey et al....

詳細記述

書誌詳細
主要な著者: Moorthy, Kohbalan, Jaber, Aws Naser, Mohd Arfian, Ismail, Ernawan, Ferda, Mohd Saberi, Mohamad, Safaai, Deris
その他の著者: Bolón-Canedo, Verónica
フォーマット: 図書の章
言語:English
English
English
出版事項: Humana Press 2019
主題:
オンライン・アクセス:http://umpir.ump.edu.my/id/eprint/25080/1/978-1-4939-9442-7_12
http://umpir.ump.edu.my/id/eprint/25080/2/66.Missing-Values%20Imputation%20Algorithms%20for%20Microarray%20Gene%20Expression%20Data.pdf
http://umpir.ump.edu.my/id/eprint/25080/3/66.1%20Missing-values%20imputation%20algorithms%20for%20microarray%20gene%20expression%20data.pdf
その他の書誌記述
要約:In gene expression studies, missing values are a common problem with important consequences for the interpretation of the final data (Satija et al., Nat Biotechnol 33(5):495, 2015). Numerous bioinformatics examination tools are used for cancer prediction, including the data set matrix (Bailey et al., Cell 173(2):371–385, 2018); thus, it is necessary to resolve the problem of missing-values imputation. This chapter presents a review of the research on missing-values imputation approaches for gene expression data. By using local and global correlation of the data, we were able to focus mostly on the differences between the algorithms. We classified the algorithms as global, hybrid, local, or knowledge-based techniques. Additionally, this chapter presents suitable assessments of the different approaches. The purpose of this review is to focus on developments in the current techniques for scientists rather than applying different or newly developed algorithms with identical functional goals. The aim was to adapt the algorithms to the characteristics of the data.