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....
| Principais autores: | , , , , , |
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| Formato: | Capítulo de Livro |
| Idioma: | English English English |
| Publicado em: |
Humana Press
2019
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| Assuntos: | |
| Acesso em linha: | 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 |
| _version_ | 1825812727142023168 |
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| author | Moorthy, Kohbalan Jaber, Aws Naser Mohd Arfian, Ismail Ernawan, Ferda Mohd Saberi, Mohamad Safaai, Deris |
| author2 | Bolón-Canedo, Verónica |
| author_facet | Bolón-Canedo, Verónica Moorthy, Kohbalan Jaber, Aws Naser Mohd Arfian, Ismail Ernawan, Ferda Mohd Saberi, Mohamad Safaai, Deris |
| author_sort | Moorthy, Kohbalan |
| collection | UMP |
| 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. |
| first_indexed | 2024-03-06T12:33:26Z |
| format | Book Chapter |
| id | UMPir25080 |
| institution | Universiti Malaysia Pahang |
| language | English English English |
| last_indexed | 2024-03-06T12:33:26Z |
| publishDate | 2019 |
| publisher | Humana Press |
| record_format | dspace |
| spelling | UMPir250802019-11-04T07:32:21Z 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 Chapter 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 |
| 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 |
| title | 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_short | Missing-values imputation algorithms for microarray gene expression data |
| title_sort | missing values imputation algorithms for microarray gene expression data |
| topic | Q Science (General) R Medicine (General) T Technology (General) |
| 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 |
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