Polymorphic BCO for protein folding model

Yudong Zhang; Yuankai Huo; Qing Zhu; Shuihua Wang; Lenan Wu

Polymorphic BCO for protein folding model

Yudong Zhang^*, Yuankai Huo, Qing Zhu, Shuihua Wang, Lenan Wu

^*Corresponding author for this work

Southeast University, Nanjing

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

To improve the protein folding simulations, polymorphic bacterial chemotaxis optimization (PBCO) was investigated on a 2D lattice model. PBCO is a novel intelligent problem solving technique inspired from the foraging behavior of bacteria. We compared PBCO with standard genetic algorithm (SGA) and Immune Genetic Algorithm (IGA) for various chain lengths. It shows that PBCO has the highest successful rate and the lowest computation time.

Original language	English
Pages (from-to)	1787-1794
Number of pages	8
Journal	Journal of Computational Information Systems
Volume	6
Issue number	6
Publication status	Published - Jun 2010
Externally published	Yes

Keywords

Immune genetic algorithm
Polymorphic bacterial chemotaxis optimization
Protein folding model
Standard genetic algorithm

Cite this

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title = "Polymorphic BCO for protein folding model",

abstract = "To improve the protein folding simulations, polymorphic bacterial chemotaxis optimization (PBCO) was investigated on a 2D lattice model. PBCO is a novel intelligent problem solving technique inspired from the foraging behavior of bacteria. We compared PBCO with standard genetic algorithm (SGA) and Immune Genetic Algorithm (IGA) for various chain lengths. It shows that PBCO has the highest successful rate and the lowest computation time.",

keywords = "Immune genetic algorithm, Polymorphic bacterial chemotaxis optimization, Protein folding model, Standard genetic algorithm",

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T1 - Polymorphic BCO for protein folding model

AU - Zhang, Yudong

AU - Huo, Yuankai

AU - Zhu, Qing

AU - Wang, Shuihua

AU - Wu, Lenan

PY - 2010/6

Y1 - 2010/6

N2 - To improve the protein folding simulations, polymorphic bacterial chemotaxis optimization (PBCO) was investigated on a 2D lattice model. PBCO is a novel intelligent problem solving technique inspired from the foraging behavior of bacteria. We compared PBCO with standard genetic algorithm (SGA) and Immune Genetic Algorithm (IGA) for various chain lengths. It shows that PBCO has the highest successful rate and the lowest computation time.

AB - To improve the protein folding simulations, polymorphic bacterial chemotaxis optimization (PBCO) was investigated on a 2D lattice model. PBCO is a novel intelligent problem solving technique inspired from the foraging behavior of bacteria. We compared PBCO with standard genetic algorithm (SGA) and Immune Genetic Algorithm (IGA) for various chain lengths. It shows that PBCO has the highest successful rate and the lowest computation time.

KW - Immune genetic algorithm

KW - Polymorphic bacterial chemotaxis optimization

KW - Protein folding model

KW - Standard genetic algorithm

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M3 - Article

AN - SCOPUS:77956946231

SN - 1553-9105

VL - 6

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JO - Journal of Computational Information Systems

JF - Journal of Computational Information Systems

IS - 6

ER -

Polymorphic BCO for protein folding model

Abstract

Keywords

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