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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/25080/1/978-1-4939-9442-7_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
http://umpir.ump.edu.my/id/eprint/25080/
https://link.springer.com/protocol/10.1007/978-1-4939-9442-7_12
https://doi.org/10.1007/978-1-4939-9442-7_12
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spelling my.ump.umpir.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
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
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/25080/1/978-1-4939-9442-7_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
http://umpir.ump.edu.my/id/eprint/25080/
https://link.springer.com/protocol/10.1007/978-1-4939-9442-7_12
https://doi.org/10.1007/978-1-4939-9442-7_12
_version_ 1651866917407817728
score 13.211869

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