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Parameter estimation using improved differential evolution and bacterial foraging algorithms to model tyrosine production in mus Musculus(mouse)

The hybrid of Differential Evolution algorithm with Kalman Filtering and Bacterial Foraging algorithm is a novel global optimisation method implemented to obtain the best kinetic parameter value. The proposed algorithm is then used to model tyrosine production in Musmusculus (mouse) by using a datas...

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Main Authors: Yeoh, Jia Xing, Chong, Chuii Khim, Mohamad, Mohd. Saberi, Choon, Yee Wen, Chai, Lian En, Deris, Safaai, Ibrahim, Zuwairie
Format: Article
Language:English
Published: Penerbit UTM Press 2015
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/58751/1/MohdSaberi2015_ParameterEstimationUsingImprovedDifferential.pdf
http://eprints.utm.my/id/eprint/58751/
http://dx.doi.org/10.11113/jt.v72.1778
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spelling my.utm.587512021-12-15T01:11:54Z http://eprints.utm.my/id/eprint/58751/ Parameter estimation using improved differential evolution and bacterial foraging algorithms to model tyrosine production in mus Musculus(mouse) Yeoh, Jia Xing Chong, Chuii Khim Mohamad, Mohd. Saberi Choon, Yee Wen Chai, Lian En Deris, Safaai Ibrahim, Zuwairie QA75 Electronic computers. Computer science The hybrid of Differential Evolution algorithm with Kalman Filtering and Bacterial Foraging algorithm is a novel global optimisation method implemented to obtain the best kinetic parameter value. The proposed algorithm is then used to model tyrosine production in Musmusculus (mouse) by using a dataset, the JAK/STAT(Janus Kinase Signal Transducer and Activator of Transcription) signal transduction pathway. Global optimisation is a method to identify the optimal kinetic parameter in ordinary differential equation. From the ordinary parameter of biomathematical field, there are many unknown parameters, and commonly, the parameter is in nonlinear form. Global optimisation method includes differential evolution algorithm, which will be used in this research. Kalman Filter and Bacterial Foraging algorithm helps in handling noise data and convergences faster respectively in the conventional Differential Evolution. The results from this experiment show estimated optimal kinetic parameters values, shorter computation time, and better accuracy of simulated results compared with other estimation algorithms. Penerbit UTM Press 2015 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/58751/1/MohdSaberi2015_ParameterEstimationUsingImprovedDifferential.pdf Yeoh, Jia Xing and Chong, Chuii Khim and Mohamad, Mohd. Saberi and Choon, Yee Wen and Chai, Lian En and Deris, Safaai and Ibrahim, Zuwairie (2015) Parameter estimation using improved differential evolution and bacterial foraging algorithms to model tyrosine production in mus Musculus(mouse). Jurnal Teknologi, 72 (1). pp. 49-56. ISSN 0127-9696 http://dx.doi.org/10.11113/jt.v72.1778 DOI:10.11113/jt.v72.1778
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Yeoh, Jia Xing
Chong, Chuii Khim
Mohamad, Mohd. Saberi
Choon, Yee Wen
Chai, Lian En
Deris, Safaai
Ibrahim, Zuwairie
Parameter estimation using improved differential evolution and bacterial foraging algorithms to model tyrosine production in mus Musculus(mouse)
description The hybrid of Differential Evolution algorithm with Kalman Filtering and Bacterial Foraging algorithm is a novel global optimisation method implemented to obtain the best kinetic parameter value. The proposed algorithm is then used to model tyrosine production in Musmusculus (mouse) by using a dataset, the JAK/STAT(Janus Kinase Signal Transducer and Activator of Transcription) signal transduction pathway. Global optimisation is a method to identify the optimal kinetic parameter in ordinary differential equation. From the ordinary parameter of biomathematical field, there are many unknown parameters, and commonly, the parameter is in nonlinear form. Global optimisation method includes differential evolution algorithm, which will be used in this research. Kalman Filter and Bacterial Foraging algorithm helps in handling noise data and convergences faster respectively in the conventional Differential Evolution. The results from this experiment show estimated optimal kinetic parameters values, shorter computation time, and better accuracy of simulated results compared with other estimation algorithms.
format Article
author Yeoh, Jia Xing
Chong, Chuii Khim
Mohamad, Mohd. Saberi
Choon, Yee Wen
Chai, Lian En
Deris, Safaai
Ibrahim, Zuwairie
author_facet Yeoh, Jia Xing
Chong, Chuii Khim
Mohamad, Mohd. Saberi
Choon, Yee Wen
Chai, Lian En
Deris, Safaai
Ibrahim, Zuwairie
author_sort Yeoh, Jia Xing
title Parameter estimation using improved differential evolution and bacterial foraging algorithms to model tyrosine production in mus Musculus(mouse)
title_short Parameter estimation using improved differential evolution and bacterial foraging algorithms to model tyrosine production in mus Musculus(mouse)
title_full Parameter estimation using improved differential evolution and bacterial foraging algorithms to model tyrosine production in mus Musculus(mouse)
title_fullStr Parameter estimation using improved differential evolution and bacterial foraging algorithms to model tyrosine production in mus Musculus(mouse)
title_full_unstemmed Parameter estimation using improved differential evolution and bacterial foraging algorithms to model tyrosine production in mus Musculus(mouse)
title_sort parameter estimation using improved differential evolution and bacterial foraging algorithms to model tyrosine production in mus musculus(mouse)
publisher Penerbit UTM Press
publishDate 2015
url http://eprints.utm.my/id/eprint/58751/1/MohdSaberi2015_ParameterEstimationUsingImprovedDifferential.pdf
http://eprints.utm.my/id/eprint/58751/
http://dx.doi.org/10.11113/jt.v72.1778
_version_ 1720436888481824768
score 13.211869

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