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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| Format: | Conference or Workshop Item |
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SPRINGER NATURE
2016
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| _version_ | 1796861554266734592 |
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| author | Lim, Sin Yi Mohamad, Mohd. Saberi Chai, Lian En Deris, Safaai Chan, Weng Howe Omatu, Sigeru Corchado, Juan Manuel Sjaugi, Muhammad Farhan Zainuddin, Muhammad Mahfuz Rajamohan, Gopinathaan Ibrahim, Zuwairie Md. Yusof, Zulkifli |
| author_facet | Lim, Sin Yi Mohamad, Mohd. Saberi Chai, Lian En Deris, Safaai Chan, Weng Howe Omatu, Sigeru Corchado, Juan Manuel Sjaugi, Muhammad Farhan Zainuddin, Muhammad Mahfuz Rajamohan, Gopinathaan Ibrahim, Zuwairie Md. Yusof, Zulkifli |
| author_sort | Lim, Sin Yi |
| collection | ePrints |
| 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. |
| first_indexed | 2024-03-05T19:58:10Z |
| format | Conference or Workshop Item |
| id | utm.eprints-66920 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:58:10Z |
| publishDate | 2016 |
| publisher | SPRINGER NATURE |
| record_format | dspace |
| spelling | utm.eprints-669202017-07-26T07:50:07Z http://eprints.utm.my/66920/ Investigation of the effects of imputation methods for gene regulatory networks modelling using dynamic bayesian networks Lim, Sin Yi Mohamad, Mohd. Saberi Chai, Lian En Deris, Safaai Chan, Weng Howe Omatu, Sigeru Corchado, Juan Manuel Sjaugi, Muhammad Farhan Zainuddin, Muhammad Mahfuz Rajamohan, Gopinathaan Ibrahim, Zuwairie Md. Yusof, Zulkifli QA75 Electronic computers. Computer science 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 NATURE 2016-01-06 Conference or Workshop Item PeerReviewed Lim, Sin Yi and Mohamad, Mohd. Saberi and Chai, Lian En and Deris, Safaai and Chan, Weng Howe and Omatu, Sigeru and Corchado, Juan Manuel and Sjaugi, Muhammad Farhan and Zainuddin, Muhammad Mahfuz and Rajamohan, Gopinathaan and Ibrahim, Zuwairie and Md. Yusof, Zulkifli (2016) Investigation of the effects of imputation methods for gene regulatory networks modelling using dynamic bayesian networks. In: 13th International Conference on Distributed Computing and Artificial Intelligence, Advances in Intelligent Systems and Computing, 1-3 Jun, 2016, Sevilla, Spain. http://link.springer.com/chapter/10.1007/978-3-319-40162-1_45 |
| spellingShingle | QA75 Electronic computers. Computer science Lim, Sin Yi Mohamad, Mohd. Saberi Chai, Lian En Deris, Safaai Chan, Weng Howe Omatu, Sigeru Corchado, Juan Manuel Sjaugi, Muhammad Farhan Zainuddin, Muhammad Mahfuz Rajamohan, Gopinathaan Ibrahim, Zuwairie Md. Yusof, Zulkifli Investigation of the effects of imputation methods for gene regulatory networks modelling using dynamic bayesian networks |
| title | 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_short | 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 |
| topic | QA75 Electronic computers. Computer science |
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