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....

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Main Authors:	Moorthy, Kohbalan, Jaber, Aws Naser, Mohd Arfian, Ismail, Ernawan, Ferda, Mohd Saberi, Mohamad, Safaai, Deris
Other Authors:	Bolón-Canedo, Verónica
Format:	Book Section
Language:	English English English
Published:	Humana Press 2019
Subjects:	Q Science (General) R Medicine (General) T Technology (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/25080/ http://umpir.ump.edu.my/id/eprint/25080/ http://umpir.ump.edu.my/id/eprint/25080/ 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

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recordtype	eprints
spelling	ump-250802019-11-04T07:32:21Z http://umpir.ump.edu.my/id/eprint/25080/ Missing-values imputation algorithms for microarray gene expression data Moorthy, Kohbalan Jaber, Aws Naser Mohd Arfian, Ismail Ernawan, Ferda Mohd Saberi, Mohamad Safaai, Deris Q Science (General) R Medicine (General) T Technology (General) 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. Humana Press Bolón-Canedo, Verónica Alonso-Betanzos, Amparo 2019-05-22 Book Section PeerReviewed text en http://umpir.ump.edu.my/id/eprint/25080/1/978-1-4939-9442-7_12 pdf en http://umpir.ump.edu.my/id/eprint/25080/2/66.Missing-Values%20Imputation%20Algorithms%20for%20Microarray%20Gene%20Expression%20Data.pdf pdf en http://umpir.ump.edu.my/id/eprint/25080/3/66.1%20Missing-values%20imputation%20algorithms%20for%20microarray%20gene%20expression%20data.pdf Moorthy, Kohbalan and Jaber, Aws Naser and Mohd Arfian, Ismail and Ernawan, Ferda and Mohd Saberi, Mohamad and Safaai, Deris (2019) Missing-values imputation algorithms for microarray gene expression data. In: Microarray Bioinformatics. Methods in Molecular Biology, 1986 . Humana Press, New York, United States, pp. 255-266. ISBN 978-1-4939-9441-0 https://link.springer.com/protocol/10.1007/978-1-4939-9442-7_12 https://doi.org/10.1007/978-1-4939-9442-7_12
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English English
topic	Q Science (General) R Medicine (General) T Technology (General)
spellingShingle	Q Science (General) R Medicine (General) T Technology (General) Moorthy, Kohbalan Jaber, Aws Naser Mohd Arfian, Ismail Ernawan, Ferda Mohd Saberi, Mohamad Safaai, Deris Missing-values imputation algorithms for microarray gene expression data
description	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.
author2	Bolón-Canedo, Verónica
author_facet	Bolón-Canedo, Verónica Moorthy, Kohbalan Jaber, Aws Naser Mohd Arfian, Ismail Ernawan, Ferda Mohd Saberi, Mohamad Safaai, Deris
format	Book Section
author	Moorthy, Kohbalan Jaber, Aws Naser Mohd Arfian, Ismail Ernawan, Ferda Mohd Saberi, Mohamad Safaai, Deris
author_sort	Moorthy, Kohbalan
title	Missing-values imputation algorithms for microarray gene expression data
title_short	Missing-values imputation algorithms for microarray gene expression data
title_full	Missing-values imputation algorithms for microarray gene expression data
title_fullStr	Missing-values imputation algorithms for microarray gene expression data
title_full_unstemmed	Missing-values imputation algorithms for microarray gene expression data
title_sort	missing-values imputation algorithms for microarray gene expression data
publisher	Humana Press
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/25080/ http://umpir.ump.edu.my/id/eprint/25080/ http://umpir.ump.edu.my/id/eprint/25080/ 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
first_indexed	2023-09-18T22:38:19Z
last_indexed	2023-09-18T22:38:19Z
_version_	1777416745611427840

Missing-values imputation algorithms for microarray gene expression data

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