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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2013: Trends and Applications in Knowledge Discovery and Data Mining pp 179–190Cite as

Parameter Estimation Using Improved Differential Evolution (IDE) and Bacterial Foraging Algorithm to Model Tyrosine Production in Mus Musculus (Mouse)

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7867))

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.

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Author information

Authors and Affiliations

  1. Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310, Johor, Malaysia

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

  2. Department of Bioprocess Engineering, Faculty of Chemical Engineering, Universiti Teknologi Malaysia, Skudai, 81310, Johor, Malaysia

    Rosli Md. Illias

Authors
  1. Jia Xing Yeoh
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  2. Chuii Khim Chong
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  3. Yee Wen Choon
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  4. Lian En Chai
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  5. Safaai Deris
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  6. Rosli Md. Illias
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  7. Mohd Saberi Mohamad
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Editor information

Editors and Affiliations

  1. School of Information Technology and Mathematical Sciences, University of South Australia, 1 Mawson Lakes Boulevard, 5095, Adelaide, SA, Australia

    Jiuyong Li

  2. Advanced Analytics Institute, University of Technology, 2-12 Blackfriars Street, Chippendale, Blackfriars Campus, 2008, Sydney, NSW, Australia

    Longbing Cao  & Can Wang  & 

  3. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore, Singapore

    Kay Chen Tan

  4. School of Automation, Guangdong University of Technology, No. 100 Waihuan Xi Road, Panyu District, 510006, Guangzhou, China

    Bo Liu

  5. School of Computing Science, Simon Fraser University, 8888 University Drive, V5A 1S6, Burnaby, BC, Canada

    Jian Pei

  6. Department of Computer Science and Information Engineering, National Cheng Kung University, No.1, University Road, 701, Tainan, Taiwan

    Vincent S. Tseng

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© 2013 Springer-Verlag Berlin Heidelberg

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Yeoh, J.X. et al. (2013). Parameter Estimation Using Improved Differential Evolution (IDE) and Bacterial Foraging Algorithm to Model Tyrosine Production in Mus Musculus (Mouse). In: Li, J., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7867. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40319-4_16

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  • DOI: https://doi.org/10.1007/978-3-642-40319-4_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40318-7

  • Online ISBN: 978-3-642-40319-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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