Investigation of the Effects of Imputation Methods for Gene Regulatory Networks Modelling Using Dynamic Bayesian Networks

DNA microarray technology plays an important role in advancing the analysis of gene expression and gene functions. However, gene expression data often contain missing values, which cause problems as most of the analysis methods of gene expression data require a complete matrix. Several missing value...

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Main Authors:	Sin, Yi Lim, Mohd Saberi, Mohamad, Lian, En Chai, Safaai, Deris, Weng, Howe Chan, Sigeru, Omatu, Muhammad Farhan, Sjaugi, Muhammad Mahfuz, Zainuddin, Gopinathaan, Rajamohan, Zuwairie, Ibrahim, Zulkifli, Md. Yusof
Format:	Book Section
Language:	English
Published:	Springer International Publishing 2016
Subjects:	TK Electrical engineering. Electronics Nuclear engineering TS Manufactures
Online Access:	http://umpir.ump.edu.my/id/eprint/13918/ http://umpir.ump.edu.my/id/eprint/13918/ http://umpir.ump.edu.my/id/eprint/13918/ http://umpir.ump.edu.my/id/eprint/13918/1/Investigation%20of%20the%20Effects%20of%20Imputation%20Methods%20for%20Gene%20Regulatory%20Networks%20Modelling%20Using%20Dynamic%20Bayesian%20Networks.pdf

id	ump-13918
recordtype	eprints
spelling	ump-139182018-02-08T02:54:32Z http://umpir.ump.edu.my/id/eprint/13918/ Investigation of the Effects of Imputation Methods for Gene Regulatory Networks Modelling Using Dynamic Bayesian Networks Sin, Yi Lim Mohd Saberi, Mohamad Lian, En Chai Safaai, Deris Weng, Howe Chan Sigeru, Omatu Muhammad Farhan, Sjaugi Muhammad Mahfuz, Zainuddin Gopinathaan, Rajamohan Zuwairie, Ibrahim Zulkifli, Md. Yusof TK Electrical engineering. Electronics Nuclear engineering TS Manufactures DNA microarray technology plays an important role in advancing the analysis of gene expression and gene functions. However, gene expression data often contain missing values, which cause problems as most of the analysis methods of gene expression data require a complete matrix. Several missing value imputation methods have been developed to overcome the problems. In this paper, effects of the missing value imputation methods in modeling of gene regulatory network are investigated. Three missing value imputation methods are used, which are k-Nearest Neighbor (kNN), Iterated Local Least Squares (ILLsimpute), and Fixed Rank Approximation Algorithm (FRAA). Dataset used in this paper is E. coli. The results suggest that the performance of each missing value imputation method is influenced by the percentage and distribution of the missing values in the dataset, which subsequently affect the modeling of gene regulatory network using Dynamic Bayesian network. Springer International Publishing 2016 Book Section PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/13918/1/Investigation%20of%20the%20Effects%20of%20Imputation%20Methods%20for%20Gene%20Regulatory%20Networks%20Modelling%20Using%20Dynamic%20Bayesian%20Networks.pdf Sin, Yi Lim and Mohd Saberi, Mohamad and Lian, En Chai and Safaai, Deris and Weng, Howe Chan and Sigeru, Omatu and Muhammad Farhan, Sjaugi and Muhammad Mahfuz, Zainuddin and Gopinathaan, Rajamohan and Zuwairie, Ibrahim and Zulkifli, Md. Yusof (2016) Investigation of the Effects of Imputation Methods for Gene Regulatory Networks Modelling Using Dynamic Bayesian Networks. In: Distributed Computing and Artificial Intelligence, 13th International Conference. Springer International Publishing, pp. 413-421. ISBN 978-3-319-40161-4 http://link.springer.com/chapter/10.1007/978-3-319-40162-1_45 DOI: 10.1007/978-3-319-40162-1_45
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering TS Manufactures
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Sin, Yi Lim Mohd Saberi, Mohamad Lian, En Chai Safaai, Deris Weng, Howe Chan Sigeru, Omatu Muhammad Farhan, Sjaugi Muhammad Mahfuz, Zainuddin Gopinathaan, Rajamohan Zuwairie, Ibrahim Zulkifli, Md. Yusof Investigation of the Effects of Imputation Methods for Gene Regulatory Networks Modelling Using Dynamic Bayesian Networks
description	DNA microarray technology plays an important role in advancing the analysis of gene expression and gene functions. However, gene expression data often contain missing values, which cause problems as most of the analysis methods of gene expression data require a complete matrix. Several missing value imputation methods have been developed to overcome the problems. In this paper, effects of the missing value imputation methods in modeling of gene regulatory network are investigated. Three missing value imputation methods are used, which are k-Nearest Neighbor (kNN), Iterated Local Least Squares (ILLsimpute), and Fixed Rank Approximation Algorithm (FRAA). Dataset used in this paper is E. coli. The results suggest that the performance of each missing value imputation method is influenced by the percentage and distribution of the missing values in the dataset, which subsequently affect the modeling of gene regulatory network using Dynamic Bayesian network.
format	Book Section
author	Sin, Yi Lim Mohd Saberi, Mohamad Lian, En Chai Safaai, Deris Weng, Howe Chan Sigeru, Omatu Muhammad Farhan, Sjaugi Muhammad Mahfuz, Zainuddin Gopinathaan, Rajamohan Zuwairie, Ibrahim Zulkifli, Md. Yusof
author_facet	Sin, Yi Lim Mohd Saberi, Mohamad Lian, En Chai Safaai, Deris Weng, Howe Chan Sigeru, Omatu Muhammad Farhan, Sjaugi Muhammad Mahfuz, Zainuddin Gopinathaan, Rajamohan Zuwairie, Ibrahim Zulkifli, Md. Yusof
author_sort	Sin, Yi Lim
title	Investigation of the Effects of Imputation Methods for Gene Regulatory Networks Modelling Using Dynamic Bayesian Networks
title_short	Investigation of the Effects of Imputation Methods for Gene Regulatory Networks Modelling Using Dynamic Bayesian Networks
title_full	Investigation of the Effects of Imputation Methods for Gene Regulatory Networks Modelling Using Dynamic Bayesian Networks
title_fullStr	Investigation of the Effects of Imputation Methods for Gene Regulatory Networks Modelling Using Dynamic Bayesian Networks
title_full_unstemmed	Investigation of the Effects of Imputation Methods for Gene Regulatory Networks Modelling Using Dynamic Bayesian Networks
title_sort	investigation of the effects of imputation methods for gene regulatory networks modelling using dynamic bayesian networks
publisher	Springer International Publishing
publishDate	2016
url	http://umpir.ump.edu.my/id/eprint/13918/ http://umpir.ump.edu.my/id/eprint/13918/ http://umpir.ump.edu.my/id/eprint/13918/ http://umpir.ump.edu.my/id/eprint/13918/1/Investigation%20of%20the%20Effects%20of%20Imputation%20Methods%20for%20Gene%20Regulatory%20Networks%20Modelling%20Using%20Dynamic%20Bayesian%20Networks.pdf
first_indexed	2023-09-18T22:17:03Z
last_indexed	2023-09-18T22:17:03Z
_version_	1777415407798321152

Investigation of the Effects of Imputation Methods for Gene Regulatory Networks Modelling Using Dynamic Bayesian Networks

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