Parameter estimation using Improved Differential Evolution (IDE) and bacterial foraging algorithm to model tyrosine production in Mus Musculus (Mouse)

Jia Xing Yeoh; Chuii Khim Chong; Yee Wen Choon; Lian En Chai; Safaai Deris; Rosli Md Illias; Mohd Saberi Mohamad

doi:10.1007/978-3-642-40319-4_16

Parameter estimation using Improved Differential Evolution (IDE) and bacterial foraging algorithm to model tyrosine production in Mus Musculus (Mouse)

Jia Xing Yeoh, Chuii Khim Chong, Yee Wen Choon, Lian En Chai, Safaai Deris, Rosli Md Illias, Mohd Saberi Mohamad

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

The hybrid of Differential Evolution algorithm with Kalman Filtering and Bacterial Foraging algorithm is a novel global optimization method that is implemented in this research to obtain the best kinetic parameter value. The proposed algorithm is then used to model tyrosine production in mus musculus (mouse) by using a dataset, JAK/STAT (Janus Kinase Signal Transducer and Activator of Transcription) signal transduction pathway. Global optimization is a method to identify the optimal kinetic parameter using ordinary differential equation. From the ordinary parameter of biomathematical field, there are many unknown parameters and commonly the parameters are in nonlinear form. Global optimization method includes differential evolution algorithm which will be used in this research. Kalman Filter and Bacterial Foraging algorithm help in handling noise data and faster convergences respectively in the conventional Differential Evolution. The results from this experiment show estimatedly optimal kinetic parameters values, shorter computation time, and better accuracy of simulated results compared with other estimation algorithms.

Original language	English
Title of host publication	Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops
Subtitle of host publication	DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, Revised Selected Papers
Pages	179-190
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-642-40319-4_16
Publication status	Published - 2013
Externally published	Yes
Event	17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013 - Gold Coast, QLD, Australia Duration: Apr 14 2013 → Apr 17 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7867 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013
Country/Territory	Australia
City	Gold Coast, QLD
Period	4/14/13 → 4/17/13

Keywords

Bacterial foraging algorithm
Differential evolution algorithm
Kalman filtering algorithm
Modeling
Parameter estimation

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-40319-4_16

Cite this

Yeoh, J. X., Chong, C. K., Choon, Y. W., Chai, L. E., Deris, S., Illias, R. M., & Mohamad, M. S. (2013). Parameter estimation using Improved Differential Evolution (IDE) and bacterial foraging algorithm to model tyrosine production in Mus Musculus (Mouse). In Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, Revised Selected Papers (pp. 179-190). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7867 LNAI). https://doi.org/10.1007/978-3-642-40319-4_16

Parameter estimation using Improved Differential Evolution (IDE) and bacterial foraging algorithm to model tyrosine production in Mus Musculus (Mouse). / Yeoh, Jia Xing; Chong, Chuii Khim; Choon, Yee Wen et al.
Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, Revised Selected Papers. 2013. p. 179-190 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7867 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yeoh, JX, Chong, CK, Choon, YW, Chai, LE, Deris, S, Illias, RM & Mohamad, MS 2013, Parameter estimation using Improved Differential Evolution (IDE) and bacterial foraging algorithm to model tyrosine production in Mus Musculus (Mouse). in Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7867 LNAI, pp. 179-190, 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013, Gold Coast, QLD, Australia, 4/14/13. https://doi.org/10.1007/978-3-642-40319-4_16

Yeoh JX, Chong CK, Choon YW, Chai LE, Deris S, Illias RM et al. Parameter estimation using Improved Differential Evolution (IDE) and bacterial foraging algorithm to model tyrosine production in Mus Musculus (Mouse). In Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, Revised Selected Papers. 2013. p. 179-190. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-40319-4_16

Yeoh, Jia Xing ; Chong, Chuii Khim ; Choon, Yee Wen et al. / Parameter estimation using Improved Differential Evolution (IDE) and bacterial foraging algorithm to model tyrosine production in Mus Musculus (Mouse). Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, Revised Selected Papers. 2013. pp. 179-190 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{96b987ddddef491d8bd62dfb32dd79f9,

title = "Parameter estimation using Improved Differential Evolution (IDE) and bacterial foraging algorithm to model tyrosine production in Mus Musculus (Mouse)",

abstract = "The hybrid of Differential Evolution algorithm with Kalman Filtering and Bacterial Foraging algorithm is a novel global optimization method that is implemented in this research to obtain the best kinetic parameter value. The proposed algorithm is then used to model tyrosine production in mus musculus (mouse) by using a dataset, JAK/STAT (Janus Kinase Signal Transducer and Activator of Transcription) signal transduction pathway. Global optimization is a method to identify the optimal kinetic parameter using ordinary differential equation. From the ordinary parameter of biomathematical field, there are many unknown parameters and commonly the parameters are in nonlinear form. Global optimization method includes differential evolution algorithm which will be used in this research. Kalman Filter and Bacterial Foraging algorithm help in handling noise data and faster convergences respectively in the conventional Differential Evolution. The results from this experiment show estimatedly optimal kinetic parameters values, shorter computation time, and better accuracy of simulated results compared with other estimation algorithms.",

keywords = "Bacterial foraging algorithm, Differential evolution algorithm, Kalman filtering algorithm, Modeling, Parameter estimation",

author = "Yeoh, {Jia Xing} and Chong, {Chuii Khim} and Choon, {Yee Wen} and Chai, {Lian En} and Safaai Deris and Illias, {Rosli Md} and Mohamad, {Mohd Saberi}",

year = "2013",

doi = "10.1007/978-3-642-40319-4_16",

language = "English",

isbn = "9783642403187",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "179--190",

booktitle = "Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops",

note = "17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013 ; Conference date: 14-04-2013 Through 17-04-2013",

}

TY - GEN

T1 - Parameter estimation using Improved Differential Evolution (IDE) and bacterial foraging algorithm to model tyrosine production in Mus Musculus (Mouse)

AU - Yeoh, Jia Xing

AU - Chong, Chuii Khim

AU - Choon, Yee Wen

AU - Chai, Lian En

AU - Deris, Safaai

AU - Illias, Rosli Md

AU - Mohamad, Mohd Saberi

PY - 2013

Y1 - 2013

N2 - The hybrid of Differential Evolution algorithm with Kalman Filtering and Bacterial Foraging algorithm is a novel global optimization method that is implemented in this research to obtain the best kinetic parameter value. The proposed algorithm is then used to model tyrosine production in mus musculus (mouse) by using a dataset, JAK/STAT (Janus Kinase Signal Transducer and Activator of Transcription) signal transduction pathway. Global optimization is a method to identify the optimal kinetic parameter using ordinary differential equation. From the ordinary parameter of biomathematical field, there are many unknown parameters and commonly the parameters are in nonlinear form. Global optimization method includes differential evolution algorithm which will be used in this research. Kalman Filter and Bacterial Foraging algorithm help in handling noise data and faster convergences respectively in the conventional Differential Evolution. The results from this experiment show estimatedly optimal kinetic parameters values, shorter computation time, and better accuracy of simulated results compared with other estimation algorithms.

AB - The hybrid of Differential Evolution algorithm with Kalman Filtering and Bacterial Foraging algorithm is a novel global optimization method that is implemented in this research to obtain the best kinetic parameter value. The proposed algorithm is then used to model tyrosine production in mus musculus (mouse) by using a dataset, JAK/STAT (Janus Kinase Signal Transducer and Activator of Transcription) signal transduction pathway. Global optimization is a method to identify the optimal kinetic parameter using ordinary differential equation. From the ordinary parameter of biomathematical field, there are many unknown parameters and commonly the parameters are in nonlinear form. Global optimization method includes differential evolution algorithm which will be used in this research. Kalman Filter and Bacterial Foraging algorithm help in handling noise data and faster convergences respectively in the conventional Differential Evolution. The results from this experiment show estimatedly optimal kinetic parameters values, shorter computation time, and better accuracy of simulated results compared with other estimation algorithms.

KW - Bacterial foraging algorithm

KW - Differential evolution algorithm

KW - Kalman filtering algorithm

KW - Modeling

KW - Parameter estimation

UR - http://www.scopus.com/inward/record.url?scp=84892838018&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84892838018&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-40319-4_16

DO - 10.1007/978-3-642-40319-4_16

M3 - Conference contribution

AN - SCOPUS:84892838018

SN - 9783642403187

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 179

EP - 190

BT - Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops

T2 - 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013

Y2 - 14 April 2013 through 17 April 2013

ER -

Parameter estimation using Improved Differential Evolution (IDE) and bacterial foraging algorithm to model tyrosine production in Mus Musculus (Mouse)

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this